Overview

Dataset statistics

Number of variables33
Number of observations9431
Missing cells24009
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory265.0 B

Variable types

DateTime2
Categorical17
Numeric13
Boolean1

Warnings

album_type has constant value "album" Constant
artist1_type has constant value "artist" Constant
artist2_type has constant value "artist" Constant
is_local has constant value "False" Constant
type has constant value "track" Constant
artistName has a high cardinality: 1072 distinct values High cardinality
trackName has a high cardinality: 2348 distinct values High cardinality
artist+track has a high cardinality: 2379 distinct values High cardinality
id_ has a high cardinality: 2346 distinct values High cardinality
album_id has a high cardinality: 1675 distinct values High cardinality
album_name has a high cardinality: 1661 distinct values High cardinality
artist1_id has a high cardinality: 1073 distinct values High cardinality
artist1_name has a high cardinality: 1073 distinct values High cardinality
artist2_id has a high cardinality: 409 distinct values High cardinality
artist2_name has a high cardinality: 408 distinct values High cardinality
name has a high cardinality: 2318 distinct values High cardinality
mode is highly correlated with album_type and 4 other fieldsHigh correlation
time_signature is highly correlated with album_type and 4 other fieldsHigh correlation
album_type is highly correlated with mode and 5 other fieldsHigh correlation
is_local is highly correlated with mode and 5 other fieldsHigh correlation
artist1_type is highly correlated with mode and 5 other fieldsHigh correlation
type is highly correlated with mode and 5 other fieldsHigh correlation
artist2_type is highly correlated with mode and 5 other fieldsHigh correlation
artist2_id has 8003 (84.9%) missing values Missing
artist2_name has 8003 (84.9%) missing values Missing
artist2_type has 8003 (84.9%) missing values Missing
msPlayed has 348 (3.7%) zeros Zeros
key has 1243 (13.2%) zeros Zeros
instrumentalness has 617 (6.5%) zeros Zeros

Reproduction

Analysis started2021-03-14 13:19:59.291373
Analysis finished2021-03-14 13:20:38.338602
Duration39.05 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Distinct5320
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Minimum2020-01-22 11:54:00
Maximum2021-01-22 18:39:00
2021-03-14T16:20:38.505812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:38.757139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

artistName
Categorical

HIGH CARDINALITY

Distinct1072
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Stromae
 
307
Gorillaz
 
266
Grimes
 
231
Hatari
 
216
IAMX
 
195
Other values (1067)
8216 

Length

Max length34
Median length10
Mean length10.31873608
Min length1

Characters and Unicode

Total characters97316
Distinct characters120
Distinct categories9 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique508 ?
Unique (%)5.4%

Sample

1st rowLudwig van Beethoven
2nd rowLudwig van Beethoven
3rd rowFrançois Couperin
4th rowLudwig van Beethoven
5th rowLudwig van Beethoven
ValueCountFrequency (%)
Stromae307
 
3.3%
Gorillaz266
 
2.8%
Grimes231
 
2.4%
Hatari216
 
2.3%
IAMX195
 
2.1%
TR/ST156
 
1.7%
Radiohead123
 
1.3%
The xx101
 
1.1%
Arcade Fire101
 
1.1%
Blonde Redhead98
 
1.0%
Other values (1062)7637
81.0%
2021-03-14T16:20:39.303904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the968
 
6.0%
stromae307
 
1.9%
gorillaz266
 
1.7%
grimes231
 
1.4%
hatari216
 
1.3%
iamx195
 
1.2%
tr/st156
 
1.0%
white136
 
0.8%
radiohead123
 
0.8%
xx102
 
0.6%
Other values (1618)13305
83.1%

Most occurring characters

ValueCountFrequency (%)
e8315
 
8.5%
a8028
 
8.2%
6574
 
6.8%
i6164
 
6.3%
r5847
 
6.0%
o5656
 
5.8%
n4976
 
5.1%
t4042
 
4.2%
s4012
 
4.1%
l3644
 
3.7%
Other values (110)40058
41.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter70294
72.2%
Uppercase Letter19594
 
20.1%
Space Separator6574
 
6.8%
Other Punctuation596
 
0.6%
Decimal Number107
 
0.1%
Dash Punctuation86
 
0.1%
Math Symbol59
 
0.1%
Other Letter5
 
< 0.1%
Currency Symbol1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e8315
11.8%
a8028
11.4%
i6164
 
8.8%
r5847
 
8.3%
o5656
 
8.0%
n4976
 
7.1%
t4042
 
5.8%
s4012
 
5.7%
l3644
 
5.2%
h3175
 
4.5%
Other values (45)16435
23.4%
ValueCountFrequency (%)
S2012
 
10.3%
T1811
 
9.2%
M1511
 
7.7%
G1189
 
6.1%
R1148
 
5.9%
B1081
 
5.5%
F1073
 
5.5%
D1054
 
5.4%
A1053
 
5.4%
C1007
 
5.1%
Other values (27)6655
34.0%
ValueCountFrequency (%)
/158
26.5%
.140
23.5%
!130
21.8%
'86
14.4%
&56
 
9.4%
,15
 
2.5%
"8
 
1.3%
:2
 
0.3%
1
 
0.2%
ValueCountFrequency (%)
924
22.4%
824
22.4%
720
18.7%
418
16.8%
311
10.3%
05
 
4.7%
23
 
2.8%
51
 
0.9%
11
 
0.9%
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
ValueCountFrequency (%)
|39
66.1%
+20
33.9%
ValueCountFrequency (%)
6574
100.0%
ValueCountFrequency (%)
-86
100.0%
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin89860
92.3%
Common7423
 
7.6%
Cyrillic28
 
< 0.1%
Hiragana3
 
< 0.1%
Han2
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e8315
 
9.3%
a8028
 
8.9%
i6164
 
6.9%
r5847
 
6.5%
o5656
 
6.3%
n4976
 
5.5%
t4042
 
4.5%
s4012
 
4.5%
l3644
 
4.1%
h3175
 
3.5%
Other values (70)36001
40.1%
ValueCountFrequency (%)
6574
88.6%
/158
 
2.1%
.140
 
1.9%
!130
 
1.8%
-86
 
1.2%
'86
 
1.2%
&56
 
0.8%
|39
 
0.5%
924
 
0.3%
824
 
0.3%
Other values (13)106
 
1.4%
ValueCountFrequency (%)
д7
25.0%
а5
17.9%
н4
14.3%
р2
 
7.1%
Х2
 
7.1%
П2
 
7.1%
Н1
 
3.6%
е1
 
3.6%
в1
 
3.6%
ы1
 
3.6%
Other values (2)2
 
7.1%
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII96621
99.3%
None662
 
0.7%
Cyrillic28
 
< 0.1%
Hiragana3
 
< 0.1%
CJK2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
e8315
 
8.6%
a8028
 
8.3%
6574
 
6.8%
i6164
 
6.4%
r5847
 
6.1%
o5656
 
5.9%
n4976
 
5.2%
t4042
 
4.2%
s4012
 
4.2%
l3644
 
3.8%
Other values (64)39363
40.7%
ValueCountFrequency (%)
é96
14.5%
ł69
10.4%
ö66
10.0%
ø59
8.9%
ò49
 
7.4%
ð40
 
6.0%
ó38
 
5.7%
ë36
 
5.4%
Ü34
 
5.1%
í32
 
4.8%
Other values (19)143
21.6%
ValueCountFrequency (%)
д7
25.0%
а5
17.9%
н4
14.3%
р2
 
7.1%
Х2
 
7.1%
П2
 
7.1%
Н1
 
3.6%
е1
 
3.6%
в1
 
3.6%
ы1
 
3.6%
Other values (2)2
 
7.1%
ValueCountFrequency (%)
1
50.0%
1
50.0%
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

trackName
Categorical

HIGH CARDINALITY

Distinct2348
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Grouch
 
48
Tanca
 
46
Exits
 
43
Niðurlút
 
43
Cradles
 
41
Other values (2343)
9210 

Length

Max length170
Median length12
Mean length16.09087053
Min length1

Characters and Unicode

Total characters151753
Distinct characters244
Distinct categories15 ?
Distinct scripts9 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1235 ?
Unique (%)13.1%

Sample

1st rowString Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro
2nd rowString Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro
3rd rowSœur Monique: 18ème ordre, 3ème livre
4th rowSeptet in E-Flat Major, Op. 20: III. Tempo di menuetto
5th rowPiano Sonata No.20 in G Major, Op. 49 No.2: II. Tempo di Menuetto
ValueCountFrequency (%)
Grouch48
 
0.5%
Tanca46
 
0.5%
Exits43
 
0.5%
Niðurlút43
 
0.5%
Cradles41
 
0.4%
bâtard41
 
0.4%
Huggin & Kissin38
 
0.4%
Drifted38
 
0.4%
Comme Des Garçons (Like The Boys)38
 
0.4%
Bicep37
 
0.4%
Other values (2338)9018
95.6%
2021-03-14T16:20:39.860426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1053
 
3.8%
974
 
3.5%
in491
 
1.8%
feat298
 
1.1%
a289
 
1.0%
of287
 
1.0%
i269
 
1.0%
you247
 
0.9%
my234
 
0.8%
no225
 
0.8%
Other values (3259)23592
84.4%

Most occurring characters

ValueCountFrequency (%)
18528
 
12.2%
e13801
 
9.1%
a8588
 
5.7%
o8576
 
5.7%
i8139
 
5.4%
n7395
 
4.9%
r7362
 
4.9%
t6700
 
4.4%
s5629
 
3.7%
l5256
 
3.5%
Other values (234)61779
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter100459
66.2%
Uppercase Letter24761
 
16.3%
Space Separator18528
 
12.2%
Other Punctuation3290
 
2.2%
Decimal Number2462
 
1.6%
Dash Punctuation964
 
0.6%
Open Punctuation577
 
0.4%
Close Punctuation577
 
0.4%
Other Letter112
 
0.1%
Final Punctuation7
 
< 0.1%
Other values (5)16
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e13801
13.7%
a8588
 
8.5%
o8576
 
8.5%
i8139
 
8.1%
n7395
 
7.4%
r7362
 
7.3%
t6700
 
6.7%
s5629
 
5.6%
l5256
 
5.2%
h3474
 
3.5%
Other values (77)25539
25.4%
ValueCountFrequency (%)
ص11
 
9.8%
ا11
 
9.8%
م11
 
9.8%
د11
 
9.8%
3
 
2.7%
2
 
1.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
ו2
 
1.8%
Other values (55)55
49.1%
ValueCountFrequency (%)
S2202
 
8.9%
T2067
 
8.3%
M1807
 
7.3%
D1520
 
6.1%
A1443
 
5.8%
I1419
 
5.7%
L1245
 
5.0%
B1187
 
4.8%
C1175
 
4.7%
O1122
 
4.5%
Other values (44)9574
38.7%
ValueCountFrequency (%)
.1268
38.5%
'568
17.3%
,503
 
15.3%
:343
 
10.4%
?160
 
4.9%
"152
 
4.6%
&117
 
3.6%
/83
 
2.5%
!58
 
1.8%
#29
 
0.9%
Other values (4)9
 
0.3%
ValueCountFrequency (%)
1549
22.3%
2518
21.0%
0374
15.2%
3171
 
6.9%
9169
 
6.9%
4156
 
6.3%
7150
 
6.1%
5141
 
5.7%
8125
 
5.1%
6109
 
4.4%
ValueCountFrequency (%)
(575
99.7%
[2
 
0.3%
ValueCountFrequency (%)
)575
99.7%
]2
 
0.3%
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
ValueCountFrequency (%)
4
57.1%
3
42.9%
ValueCountFrequency (%)
18528
100.0%
ValueCountFrequency (%)
-964
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
5
100.0%
ValueCountFrequency (%)
+5
100.0%
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin124151
81.8%
Common26420
 
17.4%
Cyrillic1069
 
0.7%
Arabic44
 
< 0.1%
Katakana30
 
< 0.1%
Han17
 
< 0.1%
Hiragana13
 
< 0.1%
Hebrew8
 
< 0.1%
Inherited1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e13801
 
11.1%
a8588
 
6.9%
o8576
 
6.9%
i8139
 
6.6%
n7395
 
6.0%
r7362
 
5.9%
t6700
 
5.4%
s5629
 
4.5%
l5256
 
4.2%
h3474
 
2.8%
Other values (81)49231
39.7%
ValueCountFrequency (%)
а136
 
12.7%
н88
 
8.2%
с82
 
7.7%
р55
 
5.1%
и53
 
5.0%
е52
 
4.9%
т49
 
4.6%
о43
 
4.0%
л42
 
3.9%
у33
 
3.1%
Other values (40)436
40.8%
ValueCountFrequency (%)
18528
70.1%
.1268
 
4.8%
-964
 
3.6%
(575
 
2.2%
)575
 
2.2%
'568
 
2.1%
1549
 
2.1%
2518
 
2.0%
,503
 
1.9%
0374
 
1.4%
Other values (27)1998
 
7.6%
ValueCountFrequency (%)
3
 
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
Other values (14)14
46.7%
ValueCountFrequency (%)
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
調1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Other values (7)7
41.2%
ValueCountFrequency (%)
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Other values (3)3
23.1%
ValueCountFrequency (%)
ו2
25.0%
ר1
12.5%
צ1
12.5%
ה1
12.5%
ש1
12.5%
ל1
12.5%
ם1
12.5%
ValueCountFrequency (%)
ص11
25.0%
ا11
25.0%
م11
25.0%
د11
25.0%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII149883
98.8%
Cyrillic1069
 
0.7%
None666
 
0.4%
Arabic44
 
< 0.1%
Katakana41
 
< 0.1%
CJK17
 
< 0.1%
Hiragana14
 
< 0.1%
Punctuation11
 
< 0.1%
Hebrew8
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
18528
 
12.4%
e13801
 
9.2%
a8588
 
5.7%
o8576
 
5.7%
i8139
 
5.4%
n7395
 
4.9%
r7362
 
4.9%
t6700
 
4.5%
s5629
 
3.8%
l5256
 
3.5%
Other values (71)59909
40.0%
ValueCountFrequency (%)
ê69
 
10.4%
á65
 
9.8%
ð61
 
9.2%
â57
 
8.6%
é52
 
7.8%
ú43
 
6.5%
ç39
 
5.9%
Ä35
 
5.3%
Þ28
 
4.2%
æ28
 
4.2%
Other values (31)189
28.4%
ValueCountFrequency (%)
а136
 
12.7%
н88
 
8.2%
с82
 
7.7%
р55
 
5.1%
и53
 
5.0%
е52
 
4.9%
т49
 
4.6%
о43
 
4.0%
л42
 
3.9%
у33
 
3.1%
Other values (40)436
40.8%
ValueCountFrequency (%)
4
36.4%
3
27.3%
3
27.3%
1
 
9.1%
ValueCountFrequency (%)
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Other values (4)4
28.6%
ValueCountFrequency (%)
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
調1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Other values (7)7
41.2%
ValueCountFrequency (%)
6
 
14.6%
5
 
12.2%
3
 
7.3%
2
 
4.9%
2
 
4.9%
2
 
4.9%
2
 
4.9%
1
 
2.4%
1
 
2.4%
1
 
2.4%
Other values (16)16
39.0%
ValueCountFrequency (%)
ص11
25.0%
ا11
25.0%
م11
25.0%
د11
25.0%
ValueCountFrequency (%)
ו2
25.0%
ר1
12.5%
צ1
12.5%
ה1
12.5%
ש1
12.5%
ל1
12.5%
ם1
12.5%

msPlayed
Real number (ℝ≥0)

ZEROS

Distinct7082
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115777.6851
Minimum0
Maximum1162123
Zeros348
Zeros (%)3.7%
Memory size147.4 KiB
2021-03-14T16:20:40.133903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q11108
median37293
Q3221729
95-th percentile342114.5
Maximum1162123
Range1162123
Interquartile range (IQR)220621

Descriptive statistics

Standard deviation134446.9228
Coefficient of variation (CV)1.161250743
Kurtosis1.89745804
Mean115777.6851
Median Absolute Deviation (MAD)37289
Skewness1.152166097
Sum1091899348
Variance1.807597505 × 1010
MonotocityNot monotonic
2021-03-14T16:20:40.398774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0348
 
3.7%
35419
 
0.2%
41017
 
0.2%
66015
 
0.2%
86114
 
0.1%
40914
 
0.1%
60514
 
0.1%
36114
 
0.1%
35314
 
0.1%
59813
 
0.1%
Other values (7072)8949
94.9%
ValueCountFrequency (%)
0348
3.7%
22
 
< 0.1%
34
 
< 0.1%
42
 
< 0.1%
54
 
< 0.1%
ValueCountFrequency (%)
11621231
< 0.1%
9871241
< 0.1%
9352421
< 0.1%
8899431
< 0.1%
8899351
< 0.1%

artist+track
Categorical

HIGH CARDINALITY

Distinct2379
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
TR/ST Grouch
 
48
Iosonouncane Tanca
 
46
Foals Exits
 
43
Hatari Niðurlút
 
43
Sub Urban Cradles
 
41
Other values (2374)
9210 

Length

Max length190
Median length24
Mean length27.40960662
Min length6

Characters and Unicode

Total characters258500
Distinct characters255
Distinct categories15 ?
Distinct scripts9 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1270 ?
Unique (%)13.5%

Sample

1st rowLudwig van Beethoven String Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro
2nd rowLudwig van Beethoven String Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro
3rd rowFrançois Couperin Sœur Monique: 18ème ordre, 3ème livre
4th rowLudwig van Beethoven Septet in E-Flat Major, Op. 20: III. Tempo di menuetto
5th rowLudwig van Beethoven Piano Sonata No.20 in G Major, Op. 49 No.2: II. Tempo di Menuetto
ValueCountFrequency (%)
TR/ST Grouch48
 
0.5%
Iosonouncane Tanca46
 
0.5%
Foals Exits43
 
0.5%
Hatari Niðurlút43
 
0.5%
Sub Urban Cradles41
 
0.4%
Stromae bâtard41
 
0.4%
Rina Sawayama Comme Des Garçons (Like The Boys)38
 
0.4%
The Shoes Drifted38
 
0.4%
Big Black Delta Huggin & Kissin38
 
0.4%
TR/ST Bicep37
 
0.4%
Other values (2369)9018
95.6%
2021-03-14T16:20:41.248244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the2021
 
4.6%
1054
 
2.4%
in541
 
1.2%
stromae343
 
0.8%
of342
 
0.8%
a328
 
0.7%
feat298
 
0.7%
i285
 
0.6%
gorillaz266
 
0.6%
you247
 
0.6%
Other values (4493)38239
87.0%

Most occurring characters

ValueCountFrequency (%)
34533
 
13.4%
e22116
 
8.6%
a16616
 
6.4%
i14303
 
5.5%
o14232
 
5.5%
r13209
 
5.1%
n12371
 
4.8%
t10742
 
4.2%
s9641
 
3.7%
l8900
 
3.4%
Other values (245)101837
39.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter170753
66.1%
Uppercase Letter44355
 
17.2%
Space Separator34533
 
13.4%
Other Punctuation3886
 
1.5%
Decimal Number2569
 
1.0%
Dash Punctuation1050
 
0.4%
Open Punctuation577
 
0.2%
Close Punctuation577
 
0.2%
Other Letter117
 
< 0.1%
Math Symbol64
 
< 0.1%
Other values (5)19
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e22116
13.0%
a16616
 
9.7%
i14303
 
8.4%
o14232
 
8.3%
r13209
 
7.7%
n12371
 
7.2%
t10742
 
6.3%
s9641
 
5.6%
l8900
 
5.2%
h6649
 
3.9%
Other values (79)41974
24.6%
ValueCountFrequency (%)
ص11
 
9.4%
ا11
 
9.4%
م11
 
9.4%
د11
 
9.4%
3
 
2.6%
2
 
1.7%
2
 
1.7%
2
 
1.7%
2
 
1.7%
2
 
1.7%
Other values (58)60
51.3%
ValueCountFrequency (%)
S4214
 
9.5%
T3878
 
8.7%
M3318
 
7.5%
D2574
 
5.8%
A2496
 
5.6%
B2268
 
5.1%
R2218
 
5.0%
C2182
 
4.9%
I2044
 
4.6%
F2010
 
4.5%
Other values (49)17153
38.7%
ValueCountFrequency (%)
.1408
36.2%
'654
16.8%
,518
 
13.3%
:345
 
8.9%
/241
 
6.2%
!188
 
4.8%
&173
 
4.5%
"160
 
4.1%
?160
 
4.1%
#29
 
0.7%
Other values (4)10
 
0.3%
ValueCountFrequency (%)
1550
21.4%
2521
20.3%
0379
14.8%
9193
 
7.5%
3182
 
7.1%
4174
 
6.8%
7170
 
6.6%
8149
 
5.8%
5142
 
5.5%
6109
 
4.2%
ValueCountFrequency (%)
(575
99.7%
[2
 
0.3%
ValueCountFrequency (%)
)575
99.7%
]2
 
0.3%
ValueCountFrequency (%)
|39
60.9%
+25
39.1%
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
ValueCountFrequency (%)
4
57.1%
3
42.9%
ValueCountFrequency (%)
34533
100.0%
ValueCountFrequency (%)
-1050
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
5
100.0%
ValueCountFrequency (%)
$2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin214011
82.8%
Common43274
 
16.7%
Cyrillic1097
 
0.4%
Arabic44
 
< 0.1%
Katakana30
 
< 0.1%
Han19
 
< 0.1%
Hiragana16
 
< 0.1%
Hebrew8
 
< 0.1%
Inherited1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e22116
 
10.3%
a16616
 
7.8%
i14303
 
6.7%
o14232
 
6.7%
r13209
 
6.2%
n12371
 
5.8%
t10742
 
5.0%
s9641
 
4.5%
l8900
 
4.2%
h6649
 
3.1%
Other values (87)85232
39.8%
ValueCountFrequency (%)
а141
 
12.9%
н92
 
8.4%
с82
 
7.5%
р57
 
5.2%
е53
 
4.8%
и53
 
4.8%
т49
 
4.5%
о44
 
4.0%
л42
 
3.8%
д36
 
3.3%
Other values (41)448
40.8%
ValueCountFrequency (%)
34533
79.8%
.1408
 
3.3%
-1050
 
2.4%
'654
 
1.5%
(575
 
1.3%
)575
 
1.3%
1550
 
1.3%
2521
 
1.2%
,518
 
1.2%
0379
 
0.9%
Other values (28)2511
 
5.8%
ValueCountFrequency (%)
3
 
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
Other values (14)14
46.7%
ValueCountFrequency (%)
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
調1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (9)9
47.4%
ValueCountFrequency (%)
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (4)4
25.0%
ValueCountFrequency (%)
ו2
25.0%
ר1
12.5%
צ1
12.5%
ה1
12.5%
ש1
12.5%
ל1
12.5%
ם1
12.5%
ValueCountFrequency (%)
ص11
25.0%
ا11
25.0%
م11
25.0%
د11
25.0%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII255935
99.0%
None1328
 
0.5%
Cyrillic1097
 
0.4%
Arabic44
 
< 0.1%
Katakana41
 
< 0.1%
CJK19
 
< 0.1%
Hiragana17
 
< 0.1%
Punctuation11
 
< 0.1%
Hebrew8
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
34533
 
13.5%
e22116
 
8.6%
a16616
 
6.5%
i14303
 
5.6%
o14232
 
5.6%
r13209
 
5.2%
n12371
 
4.8%
t10742
 
4.2%
s9641
 
3.8%
l8900
 
3.5%
Other values (72)99272
38.8%
ValueCountFrequency (%)
é148
 
11.1%
ð101
 
7.6%
ê81
 
6.1%
ö74
 
5.6%
ł72
 
5.4%
á66
 
5.0%
ú61
 
4.6%
ø60
 
4.5%
â57
 
4.3%
ò50
 
3.8%
Other values (37)558
42.0%
ValueCountFrequency (%)
а141
 
12.9%
н92
 
8.4%
с82
 
7.5%
р57
 
5.2%
е53
 
4.8%
и53
 
4.8%
т49
 
4.5%
о44
 
4.0%
л42
 
3.8%
д36
 
3.3%
Other values (41)448
40.8%
ValueCountFrequency (%)
4
36.4%
3
27.3%
3
27.3%
1
 
9.1%
ValueCountFrequency (%)
2
 
11.8%
2
 
11.8%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Other values (5)5
29.4%
ValueCountFrequency (%)
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
調1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (9)9
47.4%
ValueCountFrequency (%)
6
 
14.6%
5
 
12.2%
3
 
7.3%
2
 
4.9%
2
 
4.9%
2
 
4.9%
2
 
4.9%
1
 
2.4%
1
 
2.4%
1
 
2.4%
Other values (16)16
39.0%
ValueCountFrequency (%)
ص11
25.0%
ا11
25.0%
م11
25.0%
د11
25.0%
ValueCountFrequency (%)
ו2
25.0%
ר1
12.5%
צ1
12.5%
ה1
12.5%
ש1
12.5%
ל1
12.5%
ם1
12.5%

id_
Categorical

HIGH CARDINALITY

Distinct2346
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
47RKik1fxUhA4KAqXyS3oe
 
48
53c2c64hy7nMmx0PaRY3fX
 
46
7gVGBGI4ZqvkYBlh2LdrQy
 
43
6ZayQVBqAsaBxqWiHZHeq5
 
43
4APjoR6Fmrrl707Z1cShHS
 
41
Other values (2341)
9210 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters207482
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1256 ?
Unique (%)13.3%

Sample

1st row7vR6QjmkczDUUTQwhpeUdM
2nd row7vR6QjmkczDUUTQwhpeUdM
3rd row5CBXjtjqToJ3sw0nCPdVDU
4th row2GitKhuY13f5ZLfIXVJcNU
5th row3nnf7oDfeQ2zsnGFA3AhOP
ValueCountFrequency (%)
47RKik1fxUhA4KAqXyS3oe48
 
0.5%
53c2c64hy7nMmx0PaRY3fX46
 
0.5%
7gVGBGI4ZqvkYBlh2LdrQy43
 
0.5%
6ZayQVBqAsaBxqWiHZHeq543
 
0.5%
4APjoR6Fmrrl707Z1cShHS41
 
0.4%
18A7ha5BitZjmdHTCwXFbU41
 
0.4%
5ogtb9bGQoH8CjZNxmbNHR38
 
0.4%
43bYs8QkPdCdy5esfnmU1T38
 
0.4%
2dQvZpEkO26i4za9d746WC38
 
0.4%
0LiXd8DgPozcJVQq3QceEh37
 
0.4%
Other values (2336)9018
95.6%
2021-03-14T16:20:41.773980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
47rkik1fxuha4kaqxys3oe48
 
0.5%
53c2c64hy7nmmx0pary3fx46
 
0.5%
6zayqvbqasabxqwihzheq543
 
0.5%
7gvgbgi4zqvkyblh2ldrqy43
 
0.5%
18a7ha5bitzjmdhtcwxfbu41
 
0.4%
4apjor6fmrrl707z1cshhs41
 
0.4%
2dqvzpeko26i4za9d746wc38
 
0.4%
43bys8qkpdcdy5esfnmu1t38
 
0.4%
5ogtb9bgqoh8cjznxmbnhr38
 
0.4%
0lixd8dgpozcjvqq3qceeh37
 
0.4%
Other values (2336)9018
95.6%

Most occurring characters

ValueCountFrequency (%)
44815
 
2.3%
14788
 
2.3%
54662
 
2.2%
74411
 
2.1%
04305
 
2.1%
24298
 
2.1%
64081
 
2.0%
34079
 
2.0%
H3590
 
1.7%
b3572
 
1.7%
Other values (52)164881
79.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter83796
40.4%
Uppercase Letter81674
39.4%
Decimal Number42012
20.2%

Most frequent character per category

ValueCountFrequency (%)
b3572
 
4.3%
c3468
 
4.1%
f3443
 
4.1%
j3427
 
4.1%
e3418
 
4.1%
d3372
 
4.0%
h3350
 
4.0%
a3336
 
4.0%
q3299
 
3.9%
y3288
 
3.9%
Other values (16)49823
59.5%
ValueCountFrequency (%)
H3590
 
4.4%
B3501
 
4.3%
Q3433
 
4.2%
U3404
 
4.2%
P3334
 
4.1%
R3271
 
4.0%
Y3267
 
4.0%
C3255
 
4.0%
Z3239
 
4.0%
N3236
 
4.0%
Other values (16)48144
58.9%
ValueCountFrequency (%)
44815
11.5%
14788
11.4%
54662
11.1%
74411
10.5%
04305
10.2%
24298
10.2%
64081
9.7%
34079
9.7%
93338
7.9%
83235
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin165470
79.8%
Common42012
 
20.2%

Most frequent character per script

ValueCountFrequency (%)
H3590
 
2.2%
b3572
 
2.2%
B3501
 
2.1%
c3468
 
2.1%
f3443
 
2.1%
Q3433
 
2.1%
j3427
 
2.1%
e3418
 
2.1%
U3404
 
2.1%
d3372
 
2.0%
Other values (42)130842
79.1%
ValueCountFrequency (%)
44815
11.5%
14788
11.4%
54662
11.1%
74411
10.5%
04305
10.2%
24298
10.2%
64081
9.7%
34079
9.7%
93338
7.9%
83235
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII207482
100.0%

Most frequent character per block

ValueCountFrequency (%)
44815
 
2.3%
14788
 
2.3%
54662
 
2.2%
74411
 
2.1%
04305
 
2.1%
24298
 
2.1%
64081
 
2.0%
34079
 
2.0%
H3590
 
1.7%
b3572
 
1.7%
Other values (52)164881
79.5%

album_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
album
9431 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters47155
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalbum
2nd rowalbum
3rd rowalbum
4th rowalbum
5th rowalbum
ValueCountFrequency (%)
album9431
100.0%
2021-03-14T16:20:42.165701image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-14T16:20:42.288571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
album9431
100.0%

Most occurring characters

ValueCountFrequency (%)
a9431
20.0%
l9431
20.0%
b9431
20.0%
u9431
20.0%
m9431
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47155
100.0%

Most frequent character per category

ValueCountFrequency (%)
a9431
20.0%
l9431
20.0%
b9431
20.0%
u9431
20.0%
m9431
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47155
100.0%

Most frequent character per script

ValueCountFrequency (%)
a9431
20.0%
l9431
20.0%
b9431
20.0%
u9431
20.0%
m9431
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII47155
100.0%

Most frequent character per block

ValueCountFrequency (%)
a9431
20.0%
l9431
20.0%
b9431
20.0%
u9431
20.0%
m9431
20.0%

album_id
Categorical

HIGH CARDINALITY

Distinct1675
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
6uyslsVGFsHKzdGUosFwBM
 
210
4nT0yWWQkwLLtZdXSOKvO9
 
206
6pYNEn4tMc6gdv5fIZf5yn
 
119
4zyqNfmTrnvUejh8M1IEh9
 
105
2av2ZSHlvD7rvLSsMvtYCG
 
96
Other values (1670)
8695 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters207482
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique856 ?
Unique (%)9.1%

Sample

1st row6ClMVcmFacF795m4jJZRpx
2nd row6ClMVcmFacF795m4jJZRpx
3rd row3qsB60Pmng0d7kg1hZHIzB
4th row6ClMVcmFacF795m4jJZRpx
5th row16QCBn9N48I2Dv8K31K5RL
ValueCountFrequency (%)
6uyslsVGFsHKzdGUosFwBM210
 
2.2%
4nT0yWWQkwLLtZdXSOKvO9206
 
2.2%
6pYNEn4tMc6gdv5fIZf5yn119
 
1.3%
4zyqNfmTrnvUejh8M1IEh9105
 
1.1%
2av2ZSHlvD7rvLSsMvtYCG96
 
1.0%
4kEbcH3VT6NYfpv8Wkkk8096
 
1.0%
7bv71kWKSKlcOlhqXNNPMm94
 
1.0%
0Jxn7c5MQwqvkahOp4YiB593
 
1.0%
3stadz88XVpHcXnVYMHc4J89
 
0.9%
5nalJu58LJj7AMMyHwtbLX69
 
0.7%
Other values (1665)8254
87.5%
2021-03-14T16:20:42.696273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6uyslsvgfshkzdguosfwbm210
 
2.2%
4nt0ywwqkwlltzdxsokvo9206
 
2.2%
6pynen4tmc6gdv5fizf5yn119
 
1.3%
4zyqnfmtrnvuejh8m1ieh9105
 
1.1%
4kebch3vt6nyfpv8wkkk8096
 
1.0%
2av2zshlvd7rvlssmvtycg96
 
1.0%
7bv71kwksklcolhqxnnpmm94
 
1.0%
0jxn7c5mqwqvkahop4yib593
 
1.0%
3stadz88xvphcxnvymhc4j89
 
0.9%
5nalju58ljj7ammyhwtblx69
 
0.7%
Other values (1665)8254
87.5%

Most occurring characters

ValueCountFrequency (%)
64332
 
2.1%
54288
 
2.1%
44237
 
2.0%
14209
 
2.0%
74180
 
2.0%
04105
 
2.0%
33953
 
1.9%
s3923
 
1.9%
23783
 
1.8%
l3780
 
1.8%
Other values (52)166692
80.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter84648
40.8%
Uppercase Letter83172
40.1%
Decimal Number39662
19.1%

Most frequent character per category

ValueCountFrequency (%)
S3716
 
4.5%
H3629
 
4.4%
M3612
 
4.3%
V3474
 
4.2%
F3451
 
4.1%
W3435
 
4.1%
G3431
 
4.1%
J3401
 
4.1%
L3375
 
4.1%
N3323
 
4.0%
Other values (16)48325
58.1%
ValueCountFrequency (%)
s3923
 
4.6%
l3780
 
4.5%
k3734
 
4.4%
v3691
 
4.4%
n3629
 
4.3%
y3571
 
4.2%
a3439
 
4.1%
d3436
 
4.1%
w3368
 
4.0%
z3273
 
3.9%
Other values (16)48804
57.7%
ValueCountFrequency (%)
64332
10.9%
54288
10.8%
44237
10.7%
14209
10.6%
74180
10.5%
04105
10.3%
33953
10.0%
23783
9.5%
83496
8.8%
93079
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin167820
80.9%
Common39662
 
19.1%

Most frequent character per script

ValueCountFrequency (%)
s3923
 
2.3%
l3780
 
2.3%
k3734
 
2.2%
S3716
 
2.2%
v3691
 
2.2%
n3629
 
2.2%
H3629
 
2.2%
M3612
 
2.2%
y3571
 
2.1%
V3474
 
2.1%
Other values (42)131061
78.1%
ValueCountFrequency (%)
64332
10.9%
54288
10.8%
44237
10.7%
14209
10.6%
74180
10.5%
04105
10.3%
33953
10.0%
23783
9.5%
83496
8.8%
93079
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII207482
100.0%

Most frequent character per block

ValueCountFrequency (%)
64332
 
2.1%
54288
 
2.1%
44237
 
2.0%
14209
 
2.0%
74180
 
2.0%
04105
 
2.0%
33953
 
1.9%
s3923
 
1.9%
23783
 
1.8%
l3780
 
1.8%
Other values (52)166692
80.3%

album_name
Categorical

HIGH CARDINALITY

Distinct1661
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
racine carrée
 
210
Neyslutrans
 
206
The Witcher 3: Wild Hunt (Original Game Soundtrack)
 
119
Miss Anthropocene (Deluxe Edition)
 
105
xx
 
96
Other values (1656)
8695 

Length

Max length243
Median length13
Mean length17.40663768
Min length1

Characters and Unicode

Total characters164162
Distinct characters216
Distinct categories15 ?
Distinct scripts8 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique842 ?
Unique (%)8.9%

Sample

1st rowBeethoven for Brainpower
2nd rowBeethoven for Brainpower
3rd rowCouperin: Les Ombres Errantes
4th rowBeethoven for Brainpower
5th rowA Day with Beethoven
ValueCountFrequency (%)
racine carrée210
 
2.2%
Neyslutrans206
 
2.2%
The Witcher 3: Wild Hunt (Original Game Soundtrack)119
 
1.3%
Miss Anthropocene (Deluxe Edition)105
 
1.1%
xx96
 
1.0%
Cheese96
 
1.0%
The Destroyer - 194
 
1.0%
If You Wait (Deluxe Version)93
 
1.0%
SAWAYAMA89
 
0.9%
Rest69
 
0.7%
Other values (1651)8254
87.5%
2021-03-14T16:20:43.237767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1392
 
5.1%
612
 
2.2%
deluxe548
 
2.0%
of460
 
1.7%
edition430
 
1.6%
original245
 
0.9%
a234
 
0.9%
in231
 
0.8%
soundtrack226
 
0.8%
you222
 
0.8%
Other values (2629)22898
83.3%

Most occurring characters

ValueCountFrequency (%)
18067
 
11.0%
e15154
 
9.2%
i9780
 
6.0%
a8843
 
5.4%
n8756
 
5.3%
o8478
 
5.2%
r7935
 
4.8%
t7405
 
4.5%
s6795
 
4.1%
l5904
 
3.6%
Other values (206)67045
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter110767
67.5%
Uppercase Letter27527
 
16.8%
Space Separator18067
 
11.0%
Decimal Number2084
 
1.3%
Other Punctuation1919
 
1.2%
Open Punctuation1540
 
0.9%
Close Punctuation1540
 
0.9%
Dash Punctuation508
 
0.3%
Math Symbol63
 
< 0.1%
Other Letter63
 
< 0.1%
Other values (5)84
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e15154
13.7%
i9780
 
8.8%
a8843
 
8.0%
n8756
 
7.9%
o8478
 
7.7%
r7935
 
7.2%
t7405
 
6.7%
s6795
 
6.1%
l5904
 
5.3%
u4415
 
4.0%
Other values (67)27302
24.6%
ValueCountFrequency (%)
T2649
 
9.6%
S2540
 
9.2%
D2152
 
7.8%
A1780
 
6.5%
E1658
 
6.0%
M1632
 
5.9%
I1471
 
5.3%
B1298
 
4.7%
C1249
 
4.5%
W1212
 
4.4%
Other values (39)9886
35.9%
ValueCountFrequency (%)
4
 
6.3%
3
 
4.8%
3
 
4.8%
3
 
4.8%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
Other values (35)38
60.3%
ValueCountFrequency (%)
.505
26.3%
:456
23.8%
,301
15.7%
'273
14.2%
&166
 
8.7%
/86
 
4.5%
!39
 
2.0%
;34
 
1.8%
?29
 
1.5%
"14
 
0.7%
Other values (7)16
 
0.8%
ValueCountFrequency (%)
2493
23.7%
1466
22.4%
0370
17.8%
3216
10.4%
8111
 
5.3%
7103
 
4.9%
6100
 
4.8%
990
 
4.3%
477
 
3.7%
558
 
2.8%
ValueCountFrequency (%)
~27
42.9%
+25
39.7%
<10
 
15.9%
>1
 
1.6%
ValueCountFrequency (%)
-498
98.0%
8
 
1.6%
2
 
0.4%
ValueCountFrequency (%)
(1501
97.5%
[39
 
2.5%
ValueCountFrequency (%)
)1501
97.5%
]39
 
2.5%
ValueCountFrequency (%)
2
66.7%
1
33.3%
ValueCountFrequency (%)
18067
100.0%
ValueCountFrequency (%)
54
100.0%
ValueCountFrequency (%)
°18
100.0%
ValueCountFrequency (%)
7
100.0%
ValueCountFrequency (%)
£2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin137010
83.5%
Common25802
 
15.7%
Cyrillic1284
 
0.8%
Katakana38
 
< 0.1%
Han9
 
< 0.1%
Hiragana8
 
< 0.1%
Hebrew8
 
< 0.1%
Inherited3
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e15154
 
11.1%
i9780
 
7.1%
a8843
 
6.5%
n8756
 
6.4%
o8478
 
6.2%
r7935
 
5.8%
t7405
 
5.4%
s6795
 
5.0%
l5904
 
4.3%
u4415
 
3.2%
Other values (70)53545
39.1%
ValueCountFrequency (%)
а190
14.8%
с126
 
9.8%
л103
 
8.0%
к74
 
5.8%
и74
 
5.8%
н66
 
5.1%
р65
 
5.1%
т48
 
3.7%
з43
 
3.3%
ь40
 
3.1%
Other values (36)455
35.4%
ValueCountFrequency (%)
18067
70.0%
(1501
 
5.8%
)1501
 
5.8%
.505
 
2.0%
-498
 
1.9%
2493
 
1.9%
1466
 
1.8%
:456
 
1.8%
0370
 
1.4%
,301
 
1.2%
Other values (33)1644
 
6.4%
ValueCountFrequency (%)
4
 
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (11)13
34.2%
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
ValueCountFrequency (%)
ו2
25.0%
ר1
12.5%
צ1
12.5%
ה1
12.5%
ש1
12.5%
ל1
12.5%
ם1
12.5%
ValueCountFrequency (%)
2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII162165
98.8%
Cyrillic1284
 
0.8%
None573
 
0.3%
Punctuation63
 
< 0.1%
Katakana49
 
< 0.1%
Hiragana11
 
< 0.1%
CJK9
 
< 0.1%
Hebrew8
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
18067
 
11.1%
e15154
 
9.3%
i9780
 
6.0%
a8843
 
5.5%
n8756
 
5.4%
o8478
 
5.2%
r7935
 
4.9%
t7405
 
4.6%
s6795
 
4.2%
l5904
 
3.6%
Other values (74)65048
40.1%
ValueCountFrequency (%)
é279
48.7%
ä47
 
8.2%
ê39
 
6.8%
á29
 
5.1%
è25
 
4.4%
Ü25
 
4.4%
°18
 
3.1%
ö18
 
3.1%
ë15
 
2.6%
ñ13
 
2.3%
Other values (24)65
 
11.3%
ValueCountFrequency (%)
а190
14.8%
с126
 
9.8%
л103
 
8.0%
к74
 
5.8%
и74
 
5.8%
н66
 
5.1%
р65
 
5.1%
т48
 
3.7%
з43
 
3.3%
ь40
 
3.1%
Other values (36)455
35.4%
ValueCountFrequency (%)
54
85.7%
8
 
12.7%
1
 
1.6%
ValueCountFrequency (%)
7
14.3%
4
 
8.2%
4
 
8.2%
3
 
6.1%
3
 
6.1%
3
 
6.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
2
 
4.1%
Other values (13)17
34.7%
ValueCountFrequency (%)
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
ValueCountFrequency (%)
ו2
25.0%
ר1
12.5%
צ1
12.5%
ה1
12.5%
ש1
12.5%
ל1
12.5%
ם1
12.5%
Distinct1043
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Minimum1961-01-01 00:00:00
Maximum2021-01-26 00:00:00
2021-03-14T16:20:43.558738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:43.793609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

artist1_id
Categorical

HIGH CARDINALITY

Distinct1073
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
5j4HeCoUlzhfWtjAfM1acR
 
307
3AA28KZvwAUcZuOKwyblJQ
 
266
053q0ukIDRgzwTr4vNSwab
 
231
3yX8kCojnUAJJ1eWGZT38j
 
216
223iUzG0kb5V166FJP9ovD
 
195
Other values (1068)
8216 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters207482
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)5.4%

Sample

1st row2wOqMjp9TyABvtHdOSOTUS
2nd row2wOqMjp9TyABvtHdOSOTUS
3rd row4kVLtXuKJTE7SgYd2sQtaL
4th row2wOqMjp9TyABvtHdOSOTUS
5th row2wOqMjp9TyABvtHdOSOTUS
ValueCountFrequency (%)
5j4HeCoUlzhfWtjAfM1acR307
 
3.3%
3AA28KZvwAUcZuOKwyblJQ266
 
2.8%
053q0ukIDRgzwTr4vNSwab231
 
2.4%
3yX8kCojnUAJJ1eWGZT38j216
 
2.3%
223iUzG0kb5V166FJP9ovD195
 
2.1%
64NhyHqRKYhV0IZylrElWu156
 
1.7%
4Z8W4fKeB5YxbusRsdQVPb123
 
1.3%
3iOvXCl6edW5Um0fXEBRXy101
 
1.1%
3kjuyTCjPG1WMFCiyc5IuB101
 
1.1%
5isqImG0rLfAgBJSPMEVXF98
 
1.0%
Other values (1063)7637
81.0%
2021-03-14T16:20:44.346236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5j4hecoulzhfwtjafm1acr307
 
3.3%
3aa28kzvwauczuokwybljq266
 
2.8%
053q0ukidrgzwtr4vnswab231
 
2.4%
3yx8kcojnuajj1ewgzt38j216
 
2.3%
223iuzg0kb5v166fjp9ovd195
 
2.1%
64nhyhqrkyhv0izylrelwu156
 
1.7%
4z8w4fkeb5yxbusrsdqvpb123
 
1.3%
3iovxcl6edw5um0fxebrxy101
 
1.1%
3kjuytcjpg1wmfciyc5iub101
 
1.1%
5isqimg0rlfagbjspmevxf98
 
1.0%
Other values (1063)7637
81.0%

Most occurring characters

ValueCountFrequency (%)
35037
 
2.4%
55019
 
2.4%
14521
 
2.2%
64440
 
2.1%
04362
 
2.1%
24115
 
2.0%
44105
 
2.0%
v4102
 
2.0%
83807
 
1.8%
J3795
 
1.8%
Other values (52)164179
79.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter83363
40.2%
Lowercase Letter82588
39.8%
Decimal Number41531
20.0%

Most frequent character per category

ValueCountFrequency (%)
v4102
 
5.0%
j3716
 
4.5%
z3684
 
4.5%
y3562
 
4.3%
f3550
 
4.3%
l3504
 
4.2%
u3501
 
4.2%
b3474
 
4.2%
w3417
 
4.1%
i3284
 
4.0%
Other values (16)46794
56.7%
ValueCountFrequency (%)
J3795
 
4.6%
C3791
 
4.5%
U3748
 
4.5%
K3721
 
4.5%
X3666
 
4.4%
Z3593
 
4.3%
W3587
 
4.3%
R3547
 
4.3%
A3535
 
4.2%
B3265
 
3.9%
Other values (16)47115
56.5%
ValueCountFrequency (%)
35037
12.1%
55019
12.1%
14521
10.9%
64440
10.7%
04362
10.5%
24115
9.9%
44105
9.9%
83807
9.2%
73363
8.1%
92762
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin165951
80.0%
Common41531
 
20.0%

Most frequent character per script

ValueCountFrequency (%)
v4102
 
2.5%
J3795
 
2.3%
C3791
 
2.3%
U3748
 
2.3%
K3721
 
2.2%
j3716
 
2.2%
z3684
 
2.2%
X3666
 
2.2%
Z3593
 
2.2%
W3587
 
2.2%
Other values (42)128548
77.5%
ValueCountFrequency (%)
35037
12.1%
55019
12.1%
14521
10.9%
64440
10.7%
04362
10.5%
24115
9.9%
44105
9.9%
83807
9.2%
73363
8.1%
92762
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII207482
100.0%

Most frequent character per block

ValueCountFrequency (%)
35037
 
2.4%
55019
 
2.4%
14521
 
2.2%
64440
 
2.1%
04362
 
2.1%
24115
 
2.0%
44105
 
2.0%
v4102
 
2.0%
83807
 
1.8%
J3795
 
1.8%
Other values (52)164179
79.1%

artist1_name
Categorical

HIGH CARDINALITY

Distinct1073
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Stromae
 
307
Gorillaz
 
266
Grimes
 
231
Hatari
 
216
IAMX
 
195
Other values (1068)
8216 

Length

Max length34
Median length10
Mean length10.33103595
Min length2

Characters and Unicode

Total characters97432
Distinct characters117
Distinct categories9 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)5.4%

Sample

1st rowLudwig van Beethoven
2nd rowLudwig van Beethoven
3rd rowFrançois Couperin
4th rowLudwig van Beethoven
5th rowLudwig van Beethoven
ValueCountFrequency (%)
Stromae307
 
3.3%
Gorillaz266
 
2.8%
Grimes231
 
2.4%
Hatari216
 
2.3%
IAMX195
 
2.1%
TR/ST156
 
1.7%
Radiohead123
 
1.3%
The xx101
 
1.1%
Arcade Fire101
 
1.1%
Blonde Redhead98
 
1.0%
Other values (1063)7637
81.0%
2021-03-14T16:20:44.837993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the968
 
6.0%
stromae307
 
1.9%
gorillaz266
 
1.7%
grimes231
 
1.4%
hatari216
 
1.3%
iamx195
 
1.2%
tr/st156
 
1.0%
white136
 
0.8%
radiohead123
 
0.8%
franz102
 
0.6%
Other values (1619)13309
83.1%

Most occurring characters

ValueCountFrequency (%)
e8309
 
8.5%
a8036
 
8.2%
6578
 
6.8%
i6188
 
6.4%
r5845
 
6.0%
o5655
 
5.8%
n4966
 
5.1%
t4045
 
4.2%
s4017
 
4.1%
l3654
 
3.8%
Other values (107)40139
41.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter70404
72.3%
Uppercase Letter19578
 
20.1%
Space Separator6578
 
6.8%
Other Punctuation596
 
0.6%
Decimal Number125
 
0.1%
Dash Punctuation86
 
0.1%
Math Symbol59
 
0.1%
Other Letter5
 
< 0.1%
Currency Symbol1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e8309
11.8%
a8036
11.4%
i6188
 
8.8%
r5845
 
8.3%
o5655
 
8.0%
n4966
 
7.1%
t4045
 
5.7%
s4017
 
5.7%
l3654
 
5.2%
h3175
 
4.5%
Other values (43)16514
23.5%
ValueCountFrequency (%)
S2020
 
10.3%
T1809
 
9.2%
M1489
 
7.6%
G1188
 
6.1%
R1146
 
5.9%
B1077
 
5.5%
F1073
 
5.5%
D1060
 
5.4%
A1054
 
5.4%
C1007
 
5.1%
Other values (25)6655
34.0%
ValueCountFrequency (%)
924
19.2%
824
19.2%
720
16.0%
618
14.4%
418
14.4%
311
8.8%
05
 
4.0%
23
 
2.4%
51
 
0.8%
11
 
0.8%
ValueCountFrequency (%)
/158
26.5%
.140
23.5%
!130
21.8%
'86
14.4%
&56
 
9.4%
,15
 
2.5%
"8
 
1.3%
:2
 
0.3%
1
 
0.2%
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
ValueCountFrequency (%)
|39
66.1%
+20
33.9%
ValueCountFrequency (%)
6578
100.0%
ValueCountFrequency (%)
-86
100.0%
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin89957
92.3%
Common7445
 
7.6%
Cyrillic25
 
< 0.1%
Hiragana3
 
< 0.1%
Han2
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e8309
 
9.2%
a8036
 
8.9%
i6188
 
6.9%
r5845
 
6.5%
o5655
 
6.3%
n4966
 
5.5%
t4045
 
4.5%
s4017
 
4.5%
l3654
 
4.1%
h3175
 
3.5%
Other values (68)36067
40.1%
ValueCountFrequency (%)
6578
88.4%
/158
 
2.1%
.140
 
1.9%
!130
 
1.7%
-86
 
1.2%
'86
 
1.2%
&56
 
0.8%
|39
 
0.5%
924
 
0.3%
824
 
0.3%
Other values (14)124
 
1.7%
ValueCountFrequency (%)
д7
28.0%
а5
20.0%
н4
16.0%
р2
 
8.0%
Х2
 
8.0%
Н1
 
4.0%
е1
 
4.0%
в1
 
4.0%
ы1
 
4.0%
о1
 
4.0%
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII96759
99.3%
None643
 
0.7%
Cyrillic25
 
< 0.1%
Hiragana3
 
< 0.1%
CJK2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
e8309
 
8.6%
a8036
 
8.3%
6578
 
6.8%
i6188
 
6.4%
r5845
 
6.0%
o5655
 
5.8%
n4966
 
5.1%
t4045
 
4.2%
s4017
 
4.2%
l3654
 
3.8%
Other values (65)39466
40.8%
ValueCountFrequency (%)
é97
15.1%
ł69
10.7%
ö66
10.3%
ø59
9.2%
ð40
 
6.2%
ó38
 
5.9%
ë36
 
5.6%
Ü34
 
5.3%
í32
 
5.0%
ò31
 
4.8%
Other values (17)141
21.9%
ValueCountFrequency (%)
д7
28.0%
а5
20.0%
н4
16.0%
р2
 
8.0%
Х2
 
8.0%
Н1
 
4.0%
е1
 
4.0%
в1
 
4.0%
ы1
 
4.0%
о1
 
4.0%
ValueCountFrequency (%)
1
50.0%
1
50.0%
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

artist1_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
artist
9431 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters56586
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowartist
2nd rowartist
3rd rowartist
4th rowartist
5th rowartist
ValueCountFrequency (%)
artist9431
100.0%
2021-03-14T16:20:45.243601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-14T16:20:45.380165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
artist9431
100.0%

Most occurring characters

ValueCountFrequency (%)
t18862
33.3%
a9431
16.7%
r9431
16.7%
i9431
16.7%
s9431
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter56586
100.0%

Most frequent character per category

ValueCountFrequency (%)
t18862
33.3%
a9431
16.7%
r9431
16.7%
i9431
16.7%
s9431
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin56586
100.0%

Most frequent character per script

ValueCountFrequency (%)
t18862
33.3%
a9431
16.7%
r9431
16.7%
i9431
16.7%
s9431
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII56586
100.0%

Most frequent character per block

ValueCountFrequency (%)
t18862
33.3%
a9431
16.7%
r9431
16.7%
i9431
16.7%
s9431
16.7%

artist2_id
Categorical

HIGH CARDINALITY
MISSING

Distinct409
Distinct (%)28.6%
Missing8003
Missing (%)84.9%
Memory size147.4 KiB
1FvIWbYLm8dBezbdjucrZb
 
43
1XaPI6thQ3zTKqIU6sCvd2
 
40
5dDQASlR9FyeP4VO0jw35F
 
38
5j4HeCoUlzhfWtjAfM1acR
 
36
2x7X3t5RZDXMpcxHabxPiN
 
32
Other values (404)
1239 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters31416
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)16.9%

Sample

1st row1mIuhlEoWepcSBZp60nd57
2nd row1mIuhlEoWepcSBZp60nd57
3rd row6ACU5c6yaqQdW5u8bRNCvW
4th row3ldMTcot7OqW9irqQMP0wu
5th row5w8T4OOA2gPqsga2b69563
ValueCountFrequency (%)
1FvIWbYLm8dBezbdjucrZb43
 
0.5%
1XaPI6thQ3zTKqIU6sCvd240
 
0.4%
5dDQASlR9FyeP4VO0jw35F38
 
0.4%
5j4HeCoUlzhfWtjAfM1acR36
 
0.4%
2x7X3t5RZDXMpcxHabxPiN32
 
0.3%
4CvTDPKA6W06DRfBnZKrau23
 
0.2%
6K4I1MPd7m8IztUdtrF4YU22
 
0.2%
5W10uJRsbt9bROJDKoI1Wn20
 
0.2%
6viUqm7m1tI9x3EIo0dTLR20
 
0.2%
6x8B9oOGkqFDtUUbWf2vpP19
 
0.2%
Other values (399)1135
 
12.0%
(Missing)8003
84.9%
2021-03-14T16:20:45.731359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1fviwbylm8dbezbdjucrzb43
 
3.0%
1xapi6thq3ztkqiu6scvd240
 
2.8%
5ddqaslr9fyep4vo0jw35f38
 
2.7%
5j4hecoulzhfwtjafm1acr36
 
2.5%
2x7x3t5rzdxmpcxhabxpin32
 
2.2%
4cvtdpka6w06drfbnzkrau23
 
1.6%
6k4i1mpd7m8iztudtrf4yu22
 
1.5%
5w10ujrsbt9brojdkoi1wn20
 
1.4%
6viuqm7m1ti9x3eio0dtlr20
 
1.4%
6x8b9oogkqfdtuubwf2vpp19
 
1.3%
Other values (399)1135
79.5%

Most occurring characters

ValueCountFrequency (%)
6738
 
2.3%
0735
 
2.3%
2728
 
2.3%
5693
 
2.2%
4683
 
2.2%
I682
 
2.2%
3660
 
2.1%
1654
 
2.1%
c634
 
2.0%
d626
 
2.0%
Other values (52)24583
78.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12874
41.0%
Uppercase Letter12203
38.8%
Decimal Number6339
20.2%

Most frequent character per category

ValueCountFrequency (%)
c634
 
4.9%
d626
 
4.9%
m620
 
4.8%
v603
 
4.7%
b591
 
4.6%
t585
 
4.5%
q551
 
4.3%
o536
 
4.2%
z527
 
4.1%
f525
 
4.1%
Other values (16)7076
55.0%
ValueCountFrequency (%)
I682
 
5.6%
U616
 
5.0%
W603
 
4.9%
R569
 
4.7%
F538
 
4.4%
P537
 
4.4%
X512
 
4.2%
Q506
 
4.1%
Z498
 
4.1%
D492
 
4.0%
Other values (16)6650
54.5%
ValueCountFrequency (%)
6738
11.6%
0735
11.6%
2728
11.5%
5693
10.9%
4683
10.8%
3660
10.4%
1654
10.3%
7571
9.0%
9465
7.3%
8412
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin25077
79.8%
Common6339
 
20.2%

Most frequent character per script

ValueCountFrequency (%)
I682
 
2.7%
c634
 
2.5%
d626
 
2.5%
m620
 
2.5%
U616
 
2.5%
W603
 
2.4%
v603
 
2.4%
b591
 
2.4%
t585
 
2.3%
R569
 
2.3%
Other values (42)18948
75.6%
ValueCountFrequency (%)
6738
11.6%
0735
11.6%
2728
11.5%
5693
10.9%
4683
10.8%
3660
10.4%
1654
10.3%
7571
9.0%
9465
7.3%
8412
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII31416
100.0%

Most frequent character per block

ValueCountFrequency (%)
6738
 
2.3%
0735
 
2.3%
2728
 
2.3%
5693
 
2.2%
4683
 
2.2%
I682
 
2.2%
3660
 
2.1%
1654
 
2.1%
c634
 
2.0%
d626
 
2.0%
Other values (52)24583
78.2%

artist2_name
Categorical

HIGH CARDINALITY
MISSING

Distinct408
Distinct (%)28.6%
Missing8003
Missing (%)84.9%
Memory size147.4 KiB
GDRN
 
43
Gothenburg Symphony Orchestra
 
40
Sage
 
38
Stromae
 
36
Pavel Haas Quartet
 
32
Other values (403)
1239 

Length

Max length38
Median length12
Mean length12.70588235
Min length2

Characters and Unicode

Total characters18144
Distinct characters101
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)16.9%

Sample

1st rowLaSalle Quartet
2nd rowLaSalle Quartet
3rd rowIddo Bar-Shaï
4th rowWiener Philharmonisches Kammerensemble
5th rowYves Nat
ValueCountFrequency (%)
GDRN43
 
0.5%
Gothenburg Symphony Orchestra40
 
0.4%
Sage38
 
0.4%
Stromae36
 
0.4%
Pavel Haas Quartet32
 
0.3%
Alex Turner23
 
0.2%
Thom Yorke23
 
0.2%
Samuel T. Herring22
 
0.2%
Moses Sumney20
 
0.2%
CYBER20
 
0.2%
Other values (398)1131
 
12.0%
(Missing)8003
84.9%
2021-03-14T16:20:46.258442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
orchestra70
 
2.5%
symphony60
 
2.2%
gdrn43
 
1.6%
gothenburg40
 
1.4%
sage38
 
1.4%
quartet36
 
1.3%
stromae36
 
1.3%
pavel32
 
1.2%
haas32
 
1.2%
of30
 
1.1%
Other values (697)2347
84.9%

Most occurring characters

ValueCountFrequency (%)
a1667
 
9.2%
e1570
 
8.7%
1336
 
7.4%
r1273
 
7.0%
n1127
 
6.2%
o1060
 
5.8%
i954
 
5.3%
t691
 
3.8%
l632
 
3.5%
s620
 
3.4%
Other values (91)7214
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13643
75.2%
Uppercase Letter3046
 
16.8%
Space Separator1336
 
7.4%
Other Punctuation56
 
0.3%
Dash Punctuation21
 
0.1%
Connector Punctuation18
 
0.1%
Other Letter14
 
0.1%
Decimal Number9
 
< 0.1%
Modifier Letter1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a1667
12.2%
e1570
11.5%
r1273
 
9.3%
n1127
 
8.3%
o1060
 
7.8%
i954
 
7.0%
t691
 
5.1%
l632
 
4.6%
s620
 
4.5%
h608
 
4.5%
Other values (32)3441
25.2%
ValueCountFrequency (%)
S384
12.6%
M266
 
8.7%
B208
 
6.8%
G203
 
6.7%
P189
 
6.2%
R189
 
6.2%
D176
 
5.8%
A171
 
5.6%
T167
 
5.5%
C144
 
4.7%
Other values (20)949
31.2%
ValueCountFrequency (%)
2
14.3%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Other values (3)3
21.4%
ValueCountFrequency (%)
.25
44.6%
:25
44.6%
&3
 
5.4%
1
 
1.8%
'1
 
1.8%
!1
 
1.8%
ValueCountFrequency (%)
84
44.4%
31
 
11.1%
11
 
11.1%
91
 
11.1%
71
 
11.1%
41
 
11.1%
ValueCountFrequency (%)
1336
100.0%
ValueCountFrequency (%)
-21
100.0%
ValueCountFrequency (%)
_18
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16689
92.0%
Common1441
 
7.9%
Katakana9
 
< 0.1%
Han5
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
a1667
 
10.0%
e1570
 
9.4%
r1273
 
7.6%
n1127
 
6.8%
o1060
 
6.4%
i954
 
5.7%
t691
 
4.1%
l632
 
3.8%
s620
 
3.7%
h608
 
3.6%
Other values (62)6487
38.9%
ValueCountFrequency (%)
1336
92.7%
.25
 
1.7%
:25
 
1.7%
-21
 
1.5%
_18
 
1.2%
84
 
0.3%
&3
 
0.2%
31
 
0.1%
11
 
0.1%
91
 
0.1%
Other values (6)6
 
0.4%
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18003
99.2%
None125
 
0.7%
Katakana11
 
0.1%
CJK5
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
a1667
 
9.3%
e1570
 
8.7%
1336
 
7.4%
r1273
 
7.1%
n1127
 
6.3%
o1060
 
5.9%
i954
 
5.3%
t691
 
3.8%
l632
 
3.5%
s620
 
3.4%
Other values (56)7073
39.3%
ValueCountFrequency (%)
á27
21.6%
é25
20.0%
ł19
15.2%
ó11
8.8%
ö8
 
6.4%
ð7
 
5.6%
É6
 
4.8%
è6
 
4.8%
í2
 
1.6%
ë2
 
1.6%
Other values (10)12
9.6%
ValueCountFrequency (%)
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

artist2_type
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing8003
Missing (%)84.9%
Memory size147.4 KiB
artist
1428 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8568
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowartist
2nd rowartist
3rd rowartist
4th rowartist
5th rowartist
ValueCountFrequency (%)
artist1428
 
15.1%
(Missing)8003
84.9%
2021-03-14T16:20:46.674187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-14T16:20:46.814672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
artist1428
100.0%

Most occurring characters

ValueCountFrequency (%)
t2856
33.3%
a1428
16.7%
r1428
16.7%
i1428
16.7%
s1428
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8568
100.0%

Most frequent character per category

ValueCountFrequency (%)
t2856
33.3%
a1428
16.7%
r1428
16.7%
i1428
16.7%
s1428
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin8568
100.0%

Most frequent character per script

ValueCountFrequency (%)
t2856
33.3%
a1428
16.7%
r1428
16.7%
i1428
16.7%
s1428
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII8568
100.0%

Most frequent character per block

ValueCountFrequency (%)
t2856
33.3%
a1428
16.7%
r1428
16.7%
i1428
16.7%
s1428
16.7%

duration_ms
Real number (ℝ≥0)

Distinct2267
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263885.9661
Minimum11066
Maximum1480306
Zeros0
Zeros (%)0.0%
Memory size147.4 KiB
2021-03-14T16:20:46.947181image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum11066
5-th percentile158960
Q1209829
median245120
Q3296520
95-th percentile432133
Maximum1480306
Range1469240
Interquartile range (IQR)86691

Descriptive statistics

Standard deviation96268.86541
Coefficient of variation (CV)0.3648123727
Kurtosis13.87759009
Mean263885.9661
Median Absolute Deviation (MAD)40309
Skewness2.558170773
Sum2488708546
Variance9267694447
MonotocityNot monotonic
2021-03-14T16:20:47.209847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21386648
 
0.5%
48718646
 
0.5%
23300043
 
0.5%
35741943
 
0.5%
20882641
 
0.4%
20982941
 
0.4%
18150638
 
0.4%
23086938
 
0.4%
18000038
 
0.4%
26574638
 
0.4%
Other values (2257)9017
95.6%
ValueCountFrequency (%)
110661
 
< 0.1%
329741
 
< 0.1%
376661
 
< 0.1%
436139
0.1%
439601
 
< 0.1%
ValueCountFrequency (%)
14803061
< 0.1%
13656661
< 0.1%
11621941
< 0.1%
10830261
< 0.1%
9486401
< 0.1%

is_local
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.9 KiB
False
9431 
ValueCountFrequency (%)
False9431
100.0%
2021-03-14T16:20:47.491085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

name
Categorical

HIGH CARDINALITY

Distinct2318
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Grouch
 
48
Tanca
 
46
Niðurlút
 
43
Exits
 
43
Cradles
 
41
Other values (2313)
9210 

Length

Max length170
Median length13
Mean length16.64849963
Min length1

Characters and Unicode

Total characters157012
Distinct characters259
Distinct categories15 ?
Distinct scripts9 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1224 ?
Unique (%)13.0%

Sample

1st rowString Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro
2nd rowString Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro
3rd rowSœur Monique: 18ème ordre, 3ème livre
4th rowSeptet in E-Flat Major, Op. 20: III. Tempo di Menuetto
5th rowPiano Sonata No. 20 in G Major, Op. 49 No. 2: II. Tempo di Menuetto
ValueCountFrequency (%)
Grouch48
 
0.5%
Tanca46
 
0.5%
Niðurlút43
 
0.5%
Exits43
 
0.5%
Cradles41
 
0.4%
bâtard41
 
0.4%
Drifted38
 
0.4%
Comme Des Garçons (Like The Boys)38
 
0.4%
Huggin & Kissin38
 
0.4%
Bicep37
 
0.4%
Other values (2308)9018
95.6%
2021-03-14T16:20:48.060966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1137
 
3.9%
the1070
 
3.7%
in491
 
1.7%
feat301
 
1.0%
of292
 
1.0%
a288
 
1.0%
i268
 
0.9%
no250
 
0.9%
you247
 
0.9%
my234
 
0.8%
Other values (3325)24241
84.1%

Most occurring characters

ValueCountFrequency (%)
19388
 
12.3%
e14184
 
9.0%
a8879
 
5.7%
o8859
 
5.6%
i8475
 
5.4%
r7648
 
4.9%
n7593
 
4.8%
t6916
 
4.4%
s5796
 
3.7%
l5365
 
3.4%
Other values (249)63909
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103572
66.0%
Uppercase Letter25482
 
16.2%
Space Separator19388
 
12.3%
Other Punctuation3380
 
2.2%
Decimal Number2559
 
1.6%
Dash Punctuation1132
 
0.7%
Open Punctuation630
 
0.4%
Close Punctuation630
 
0.4%
Other Letter202
 
0.1%
Final Punctuation21
 
< 0.1%
Other values (5)16
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e14184
13.7%
a8879
 
8.6%
o8859
 
8.6%
i8475
 
8.2%
r7648
 
7.4%
n7593
 
7.3%
t6916
 
6.7%
s5796
 
5.6%
l5365
 
5.2%
h3508
 
3.4%
Other values (77)26349
25.4%
ValueCountFrequency (%)
13
 
6.4%
ص11
 
5.4%
ا11
 
5.4%
م11
 
5.4%
د11
 
5.4%
5
 
2.5%
5
 
2.5%
5
 
2.5%
5
 
2.5%
5
 
2.5%
Other values (70)120
59.4%
ValueCountFrequency (%)
S2280
 
8.9%
T2083
 
8.2%
M1911
 
7.5%
D1540
 
6.0%
A1447
 
5.7%
I1427
 
5.6%
L1291
 
5.1%
B1211
 
4.8%
C1209
 
4.7%
O1155
 
4.5%
Other values (44)9928
39.0%
ValueCountFrequency (%)
.1280
37.9%
'570
16.9%
,549
16.2%
:362
 
10.7%
?160
 
4.7%
"154
 
4.6%
&119
 
3.5%
/86
 
2.5%
!58
 
1.7%
#29
 
0.9%
Other values (5)13
 
0.4%
ValueCountFrequency (%)
1567
22.2%
2538
21.0%
0391
15.3%
9181
 
7.1%
3179
 
7.0%
4164
 
6.4%
7152
 
5.9%
5145
 
5.7%
8130
 
5.1%
6112
 
4.4%
ValueCountFrequency (%)
(614
97.5%
[16
 
2.5%
ValueCountFrequency (%)
)614
97.5%
]16
 
2.5%
ValueCountFrequency (%)
17
81.0%
4
 
19.0%
ValueCountFrequency (%)
19388
100.0%
ValueCountFrequency (%)
-1132
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
5
100.0%
ValueCountFrequency (%)
+5
100.0%
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin127985
81.5%
Common27755
 
17.7%
Cyrillic1069
 
0.7%
Han63
 
< 0.1%
Hiragana57
 
< 0.1%
Arabic44
 
< 0.1%
Katakana30
 
< 0.1%
Hebrew8
 
< 0.1%
Inherited1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e14184
 
11.1%
a8879
 
6.9%
o8859
 
6.9%
i8475
 
6.6%
r7648
 
6.0%
n7593
 
5.9%
t6916
 
5.4%
s5796
 
4.5%
l5365
 
4.2%
h3508
 
2.7%
Other values (81)50762
39.7%
ValueCountFrequency (%)
а136
 
12.7%
н88
 
8.2%
с82
 
7.7%
р55
 
5.1%
и53
 
5.0%
е52
 
4.9%
т49
 
4.6%
о43
 
4.0%
л42
 
3.9%
у33
 
3.1%
Other values (40)436
40.8%
ValueCountFrequency (%)
19388
69.9%
.1280
 
4.6%
-1132
 
4.1%
(614
 
2.2%
)614
 
2.2%
'570
 
2.1%
1567
 
2.0%
,549
 
2.0%
2538
 
1.9%
0391
 
1.4%
Other values (27)2112
 
7.6%
ValueCountFrequency (%)
5
 
7.9%
5
 
7.9%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
Other values (18)21
33.3%
ValueCountFrequency (%)
3
 
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
Other values (14)14
46.7%
ValueCountFrequency (%)
13
22.8%
5
 
8.8%
5
 
8.8%
5
 
8.8%
5
 
8.8%
4
 
7.0%
4
 
7.0%
4
 
7.0%
4
 
7.0%
1
 
1.8%
Other values (7)7
12.3%
ValueCountFrequency (%)
ו2
25.0%
ר1
12.5%
צ1
12.5%
ה1
12.5%
ש1
12.5%
ל1
12.5%
ם1
12.5%
ValueCountFrequency (%)
ص11
25.0%
ا11
25.0%
م11
25.0%
د11
25.0%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII155004
98.7%
Cyrillic1069
 
0.7%
None700
 
0.4%
CJK63
 
< 0.1%
Hiragana58
 
< 0.1%
Arabic44
 
< 0.1%
Katakana41
 
< 0.1%
Punctuation25
 
< 0.1%
Hebrew8
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
19388
 
12.5%
e14184
 
9.2%
a8879
 
5.7%
o8859
 
5.7%
i8475
 
5.5%
r7648
 
4.9%
n7593
 
4.9%
t6916
 
4.5%
s5796
 
3.7%
l5365
 
3.5%
Other values (71)61901
39.9%
ValueCountFrequency (%)
ð72
 
10.3%
ê70
 
10.0%
á65
 
9.3%
é63
 
9.0%
â57
 
8.1%
ú43
 
6.1%
ç39
 
5.6%
Ä35
 
5.0%
è30
 
4.3%
Þ28
 
4.0%
Other values (32)198
28.3%
ValueCountFrequency (%)
а136
 
12.7%
н88
 
8.2%
с82
 
7.7%
р55
 
5.1%
и53
 
5.0%
е52
 
4.9%
т49
 
4.6%
о43
 
4.0%
л42
 
3.9%
у33
 
3.1%
Other values (40)436
40.8%
ValueCountFrequency (%)
13
22.4%
5
 
8.6%
5
 
8.6%
5
 
8.6%
5
 
8.6%
4
 
6.9%
4
 
6.9%
4
 
6.9%
4
 
6.9%
1
 
1.7%
Other values (8)8
13.8%
ValueCountFrequency (%)
5
 
7.9%
5
 
7.9%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
4
 
6.3%
Other values (18)21
33.3%
ValueCountFrequency (%)
6
 
14.6%
5
 
12.2%
3
 
7.3%
2
 
4.9%
2
 
4.9%
2
 
4.9%
2
 
4.9%
1
 
2.4%
1
 
2.4%
1
 
2.4%
Other values (16)16
39.0%
ValueCountFrequency (%)
17
68.0%
4
 
16.0%
4
 
16.0%
ValueCountFrequency (%)
ص11
25.0%
ا11
25.0%
م11
25.0%
د11
25.0%
ValueCountFrequency (%)
ו2
25.0%
ר1
12.5%
צ1
12.5%
ה1
12.5%
ש1
12.5%
ל1
12.5%
ם1
12.5%

popularity
Real number (ℝ≥0)

Distinct92
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.47216626
Minimum0
Maximum96
Zeros47
Zeros (%)0.5%
Memory size147.4 KiB
2021-03-14T16:20:48.328989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q134
median45
Q357
95-th percentile72
Maximum96
Range96
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.69922447
Coefficient of variation (CV)0.3672405747
Kurtosis-0.09580275519
Mean45.47216626
Median Absolute Deviation (MAD)11
Skewness-0.1944020984
Sum428848
Variance278.8640979
MonotocityNot monotonic
2021-03-14T16:20:48.612192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36376
 
4.0%
34324
 
3.4%
58263
 
2.8%
57254
 
2.7%
49252
 
2.7%
37237
 
2.5%
38236
 
2.5%
44235
 
2.5%
51229
 
2.4%
52218
 
2.3%
Other values (82)6807
72.2%
ValueCountFrequency (%)
047
0.5%
112
 
0.1%
236
0.4%
336
0.4%
43
 
< 0.1%
ValueCountFrequency (%)
964
< 0.1%
951
 
< 0.1%
921
 
< 0.1%
913
< 0.1%
891
 
< 0.1%

type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
track
9431 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters47155
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrack
2nd rowtrack
3rd rowtrack
4th rowtrack
5th rowtrack
ValueCountFrequency (%)
track9431
100.0%
2021-03-14T16:20:49.034370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-14T16:20:49.170965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
track9431
100.0%

Most occurring characters

ValueCountFrequency (%)
t9431
20.0%
r9431
20.0%
a9431
20.0%
c9431
20.0%
k9431
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47155
100.0%

Most frequent character per category

ValueCountFrequency (%)
t9431
20.0%
r9431
20.0%
a9431
20.0%
c9431
20.0%
k9431
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47155
100.0%

Most frequent character per script

ValueCountFrequency (%)
t9431
20.0%
r9431
20.0%
a9431
20.0%
c9431
20.0%
k9431
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII47155
100.0%

Most frequent character per block

ValueCountFrequency (%)
t9431
20.0%
r9431
20.0%
a9431
20.0%
c9431
20.0%
k9431
20.0%

danceability
Real number (ℝ≥0)

Distinct709
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5618687202
Minimum0
Maximum0.957
Zeros1
Zeros (%)< 0.1%
Memory size147.4 KiB
2021-03-14T16:20:49.283491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.255
Q10.476
median0.586
Q30.67
95-th percentile0.798
Maximum0.957
Range0.957
Interquartile range (IQR)0.194

Descriptive statistics

Standard deviation0.1595507275
Coefficient of variation (CV)0.2839644241
Kurtosis0.1197417107
Mean0.5618687202
Median Absolute Deviation (MAD)0.098
Skewness-0.5526675629
Sum5298.9839
Variance0.02545643465
MonotocityNot monotonic
2021-03-14T16:20:49.539859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.629100
 
1.1%
0.64685
 
0.9%
0.58184
 
0.9%
0.58779
 
0.8%
0.5476
 
0.8%
0.48368
 
0.7%
0.63468
 
0.7%
0.70566
 
0.7%
0.58665
 
0.7%
0.6362
 
0.7%
Other values (699)8678
92.0%
ValueCountFrequency (%)
01
< 0.1%
0.05812
< 0.1%
0.06271
< 0.1%
0.06361
< 0.1%
0.06461
< 0.1%
ValueCountFrequency (%)
0.9572
 
< 0.1%
0.95611
0.1%
0.9421
 
< 0.1%
0.944
 
< 0.1%
0.9372
 
< 0.1%

energy
Real number (ℝ≥0)

Distinct893
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6684093156
Minimum0.000985
Maximum0.998
Zeros0
Zeros (%)0.0%
Memory size147.4 KiB
2021-03-14T16:20:49.825330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.000985
5-th percentile0.229
Q10.5445
median0.707
Q30.842
95-th percentile0.943
Maximum0.998
Range0.997015
Interquartile range (IQR)0.2975

Descriptive statistics

Standard deviation0.2175198109
Coefficient of variation (CV)0.3254290534
Kurtosis0.3094997029
Mean0.6684093156
Median Absolute Deviation (MAD)0.145
Skewness-0.859534261
Sum6303.768255
Variance0.04731486812
MonotocityNot monotonic
2021-03-14T16:20:50.461903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.59784
 
0.9%
0.7281
 
0.9%
0.86977
 
0.8%
0.91272
 
0.8%
0.58969
 
0.7%
0.6764
 
0.7%
0.83564
 
0.7%
0.82562
 
0.7%
0.75962
 
0.7%
0.84261
 
0.6%
Other values (883)8735
92.6%
ValueCountFrequency (%)
0.0009851
< 0.1%
0.001061
< 0.1%
0.002282
< 0.1%
0.002581
< 0.1%
0.002831
< 0.1%
ValueCountFrequency (%)
0.9984
 
< 0.1%
0.9956
 
0.1%
0.9911
 
< 0.1%
0.9918
0.2%
0.9884
 
< 0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.38267416
Minimum0
Maximum11
Zeros1243
Zeros (%)13.2%
Memory size147.4 KiB
2021-03-14T16:20:50.685307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.701106552
Coefficient of variation (CV)0.6875962472
Kurtosis-1.36861152
Mean5.38267416
Median Absolute Deviation (MAD)3
Skewness-0.05287628203
Sum50764
Variance13.69818971
MonotocityNot monotonic
2021-03-14T16:20:50.890108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
01243
13.2%
91079
11.4%
7990
10.5%
1907
9.6%
2892
9.5%
11858
9.1%
10670
7.1%
5660
7.0%
8639
6.8%
4621
6.6%
Other values (2)872
9.2%
ValueCountFrequency (%)
01243
13.2%
1907
9.6%
2892
9.5%
3278
 
2.9%
4621
6.6%
ValueCountFrequency (%)
11858
9.1%
10670
7.1%
91079
11.4%
8639
6.8%
7990
10.5%

loudness
Real number (ℝ)

Distinct2157
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.30066239
Minimum-40.385
Maximum-1.502
Zeros0
Zeros (%)0.0%
Memory size147.4 KiB
2021-03-14T16:20:51.131795image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-40.385
5-th percentile-15.709
Q1-9.838
median-7.51
Q3-5.597
95-th percentile-3.502
Maximum-1.502
Range38.883
Interquartile range (IQR)4.241

Descriptive statistics

Standard deviation4.336175192
Coefficient of variation (CV)-0.5223890562
Kurtosis8.074792922
Mean-8.30066239
Median Absolute Deviation (MAD)2.127
Skewness-2.214979456
Sum-78283.547
Variance18.8024153
MonotocityNot monotonic
2021-03-14T16:20:51.373029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.10150
 
0.5%
-8.25148
 
0.5%
-10.00946
 
0.5%
-6.93143
 
0.5%
-2.9443
 
0.5%
-1.8741
 
0.4%
-9.65241
 
0.4%
-3.77638
 
0.4%
-8.17138
 
0.4%
-3.74738
 
0.4%
Other values (2147)9005
95.5%
ValueCountFrequency (%)
-40.3851
< 0.1%
-38.8632
< 0.1%
-37.7271
< 0.1%
-37.4222
< 0.1%
-37.261
< 0.1%
ValueCountFrequency (%)
-1.5022
 
< 0.1%
-1.5891
 
< 0.1%
-1.6731
 
< 0.1%
-1.7391
 
< 0.1%
-1.74217
0.2%

mode
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
1
4898 
0
4533 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
14898
51.9%
04533
48.1%
2021-03-14T16:20:51.843869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-14T16:20:51.982358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
14898
51.9%
04533
48.1%

Most occurring characters

ValueCountFrequency (%)
14898
51.9%
04533
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9431
100.0%

Most frequent character per category

ValueCountFrequency (%)
14898
51.9%
04533
48.1%

Most occurring scripts

ValueCountFrequency (%)
Common9431
100.0%

Most frequent character per script

ValueCountFrequency (%)
14898
51.9%
04533
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9431
100.0%

Most frequent character per block

ValueCountFrequency (%)
14898
51.9%
04533
48.1%

speechiness
Real number (ℝ≥0)

Distinct717
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06396315343
Minimum0
Maximum0.95
Zeros1
Zeros (%)< 0.1%
Memory size147.4 KiB
2021-03-14T16:20:52.105649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0282
Q10.0345
median0.0429
Q30.0621
95-th percentile0.19
Maximum0.95
Range0.95
Interquartile range (IQR)0.0276

Descriptive statistics

Standard deviation0.06268440319
Coefficient of variation (CV)0.9800080176
Kurtosis25.95308038
Mean0.06396315343
Median Absolute Deviation (MAD)0.0108
Skewness4.18014268
Sum603.2365
Variance0.003929334404
MonotocityNot monotonic
2021-03-14T16:20:52.339989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0376109
 
1.2%
0.03492
 
1.0%
0.032386
 
0.9%
0.028281
 
0.9%
0.044171
 
0.8%
0.034669
 
0.7%
0.041769
 
0.7%
0.03566
 
0.7%
0.032164
 
0.7%
0.036662
 
0.7%
Other values (707)8662
91.8%
ValueCountFrequency (%)
01
< 0.1%
0.02421
< 0.1%
0.02441
< 0.1%
0.02471
< 0.1%
0.02481
< 0.1%
ValueCountFrequency (%)
0.951
< 0.1%
0.9071
< 0.1%
0.881
< 0.1%
0.8041
< 0.1%
0.6361
< 0.1%

acousticness
Real number (ℝ≥0)

Distinct1544
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1886488729
Minimum2.38 × 106
Maximum0.996
Zeros0
Zeros (%)0.0%
Memory size147.4 KiB
2021-03-14T16:20:52.576211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.38 × 106
5-th percentile0.000144
Q10.00463
median0.0458
Q30.245
95-th percentile0.8985
Maximum0.996
Range0.99599762
Interquartile range (IQR)0.24037

Descriptive statistics

Standard deviation0.2799912069
Coefficient of variation (CV)1.484192312
Kurtosis1.439955355
Mean0.1886488729
Median Absolute Deviation (MAD)0.045217
Skewness1.632571472
Sum1779.147521
Variance0.07839507592
MonotocityNot monotonic
2021-03-14T16:20:52.827181image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024658
 
0.6%
0.0013250
 
0.5%
0.091548
 
0.5%
0.28645
 
0.5%
0.055944
 
0.5%
0.11944
 
0.5%
0.12943
 
0.5%
0.0001543
 
0.5%
0.2742
 
0.4%
0.11742
 
0.4%
Other values (1534)8972
95.1%
ValueCountFrequency (%)
2.38 × 1061
 
< 0.1%
2.93 × 1063
< 0.1%
2.96 × 1064
< 0.1%
3.21 × 1064
< 0.1%
3.3 × 1066
0.1%
ValueCountFrequency (%)
0.9963
 
< 0.1%
0.99514
0.1%
0.99411
0.1%
0.9935
 
0.1%
0.9925
 
0.1%

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct1455
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2648508546
Minimum0
Maximum0.988
Zeros617
Zeros (%)6.5%
Memory size147.4 KiB
2021-03-14T16:20:53.065104image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0005275
median0.0352
Q30.583
95-th percentile0.901
Maximum0.988
Range0.988
Interquartile range (IQR)0.5824725

Descriptive statistics

Standard deviation0.3445858208
Coefficient of variation (CV)1.301056103
Kurtosis-0.9019074121
Mean0.2648508546
Median Absolute Deviation (MAD)0.0352
Skewness0.8941568942
Sum2497.80841
Variance0.1187393879
MonotocityNot monotonic
2021-03-14T16:20:53.309583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0617
 
6.5%
0.0030849
 
0.5%
4.94 × 10548
 
0.5%
0.80647
 
0.5%
0.00029646
 
0.5%
0.029644
 
0.5%
0.0010743
 
0.5%
0.5443
 
0.5%
0.69243
 
0.5%
6.48 × 10541
 
0.4%
Other values (1445)8410
89.2%
ValueCountFrequency (%)
0617
6.5%
1 × 10618
 
0.2%
1.03 × 1066
 
0.1%
1.1 × 1065
 
0.1%
1.15 × 10620
 
0.2%
ValueCountFrequency (%)
0.9881
 
< 0.1%
0.9871
 
< 0.1%
0.9793
< 0.1%
0.9771
 
< 0.1%
0.9762
< 0.1%

liveness
Real number (ℝ≥0)

Distinct788
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1901532393
Minimum0.023
Maximum0.982
Zeros0
Zeros (%)0.0%
Memory size147.4 KiB
2021-03-14T16:20:53.553687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.023
5-th percentile0.0635
Q10.09545
median0.127
Q30.245
95-th percentile0.514
Maximum0.982
Range0.959
Interquartile range (IQR)0.14955

Descriptive statistics

Standard deviation0.1515360681
Coefficient of variation (CV)0.7969155229
Kurtosis5.120280073
Mean0.1901532393
Median Absolute Deviation (MAD)0.0453
Skewness2.120677612
Sum1793.3352
Variance0.02296317995
MonotocityNot monotonic
2021-03-14T16:20:53.837893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.112153
 
1.6%
0.111124
 
1.3%
0.11119
 
1.3%
0.105115
 
1.2%
0.106112
 
1.2%
0.103103
 
1.1%
0.14289
 
0.9%
0.12383
 
0.9%
0.13582
 
0.9%
0.20581
 
0.9%
Other values (778)8370
88.7%
ValueCountFrequency (%)
0.0231
 
< 0.1%
0.02511
 
< 0.1%
0.029810
0.1%
0.0321
 
< 0.1%
0.032218
0.2%
ValueCountFrequency (%)
0.9821
 
< 0.1%
0.981
 
< 0.1%
0.9471
 
< 0.1%
0.93414
0.1%
0.9292
 
< 0.1%

valence
Real number (ℝ≥0)

Distinct986
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4140715619
Minimum0
Maximum0.976
Zeros1
Zeros (%)< 0.1%
Memory size147.4 KiB
2021-03-14T16:20:54.097397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05275
Q10.198
median0.394
Q30.594
95-th percentile0.851
Maximum0.976
Range0.976
Interquartile range (IQR)0.396

Descriptive statistics

Standard deviation0.2481769174
Coefficient of variation (CV)0.5993575514
Kurtosis-0.9020447413
Mean0.4140715619
Median Absolute Deviation (MAD)0.199
Skewness0.2965320497
Sum3905.1089
Variance0.06159178235
MonotocityNot monotonic
2021-03-14T16:20:54.353240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44365
 
0.7%
0.48662
 
0.7%
0.77758
 
0.6%
0.27658
 
0.6%
0.6557
 
0.6%
0.059257
 
0.6%
0.30253
 
0.6%
0.17551
 
0.5%
0.78551
 
0.5%
0.39250
 
0.5%
Other values (976)8869
94.0%
ValueCountFrequency (%)
01
< 0.1%
0.02251
< 0.1%
0.02521
< 0.1%
0.02981
< 0.1%
0.03051
< 0.1%
ValueCountFrequency (%)
0.9768
0.1%
0.9722
 
< 0.1%
0.973
 
< 0.1%
0.9691
 
< 0.1%
0.9688
0.1%

tempo
Real number (ℝ≥0)

Distinct2215
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.2954269
Minimum0
Maximum219.905
Zeros1
Zeros (%)< 0.1%
Memory size147.4 KiB
2021-03-14T16:20:54.597113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.999
Q1102.001
median119.973
Q3130.331
95-th percentile168.063
Maximum219.905
Range219.905
Interquartile range (IQR)28.33

Descriptive statistics

Standard deviation24.72991837
Coefficient of variation (CV)0.207299802
Kurtosis0.5332078113
Mean119.2954269
Median Absolute Deviation (MAD)15.007
Skewness0.4530466989
Sum1125075.171
Variance611.5688627
MonotocityNot monotonic
2021-03-14T16:20:54.837952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124.01349
 
0.5%
95.0746
 
0.5%
119.97644
 
0.5%
188.05543
 
0.5%
126.01241
 
0.4%
67.44341
 
0.4%
120.01240
 
0.4%
119.16238
 
0.4%
108.00238
 
0.4%
136.4438
 
0.4%
Other values (2205)9013
95.6%
ValueCountFrequency (%)
01
 
< 0.1%
52.391
 
< 0.1%
55.80313
0.1%
58.1442
 
< 0.1%
59.9854
 
< 0.1%
ValueCountFrequency (%)
219.9051
 
< 0.1%
218.2311
 
< 0.1%
214.677
0.1%
209.911
 
< 0.1%
203.3571
 
< 0.1%

time_signature
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
4
8840 
3
 
422
5
 
114
1
 
54
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9431
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row3
ValueCountFrequency (%)
48840
93.7%
3422
 
4.5%
5114
 
1.2%
154
 
0.6%
01
 
< 0.1%
2021-03-14T16:20:55.264956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-14T16:20:55.401442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
48840
93.7%
3422
 
4.5%
5114
 
1.2%
154
 
0.6%
01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
48840
93.7%
3422
 
4.5%
5114
 
1.2%
154
 
0.6%
01
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9431
100.0%

Most frequent character per category

ValueCountFrequency (%)
48840
93.7%
3422
 
4.5%
5114
 
1.2%
154
 
0.6%
01
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common9431
100.0%

Most frequent character per script

ValueCountFrequency (%)
48840
93.7%
3422
 
4.5%
5114
 
1.2%
154
 
0.6%
01
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9431
100.0%

Most frequent character per block

ValueCountFrequency (%)
48840
93.7%
3422
 
4.5%
5114
 
1.2%
154
 
0.6%
01
 
< 0.1%

Interactions

2021-03-14T16:20:06.026498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:06.246428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:06.405455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:06.580284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:06.740161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:06.895541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:07.069839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:07.254703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:07.487773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:07.656317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:07.831611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:08.010253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:08.176347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:08.385593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:08.562386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:08.773522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:08.986758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:09.171005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:09.487834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:09.672687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:09.856525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:10.050975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:10.229213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:10.416068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:10.590776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:10.748864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:10.937919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:11.106131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:11.265382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:11.424500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:11.607769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:11.767723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:11.937620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:12.114047image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:12.277685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:12.441959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:12.597268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:12.775470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:12.971378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:13.133533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:13.321201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:13.480839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:13.661991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:13.810915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:13.984931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:14.142576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:14.306540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:14.458772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:14.755560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:14.970428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:15.159738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:15.319326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:15.467440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:15.630365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:15.801196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:15.977347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:16.163713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:16.338850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:16.533154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:16.714011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:16.928844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:17.132688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:17.326936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:17.488535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:17.676997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:17.895871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:18.151484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:18.319079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:18.504954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:18.692475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:18.867349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:19.040812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:19.213762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:19.450485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:19.680842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:19.862168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:20.090515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:20.268246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:20.462344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:20.634358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:20.815638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:21.007158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:21.233158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:21.417442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:21.761164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:21.963896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:22.132953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:22.310094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:22.482463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:22.641910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:22.806822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:22.977878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:23.182351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:23.392985image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:23.597774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:23.822432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:24.053297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:24.335515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:24.603162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:24.824468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:25.013581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:25.225912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:25.388283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:25.565779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:25.735202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:25.898922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:26.108331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:26.292461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:26.481727image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:26.669117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:26.849390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:27.052974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:27.240736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:27.410645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:27.570941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:27.747155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:27.936990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:28.115812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:28.302311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:28.452954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:28.638775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:28.814648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:28.992792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:29.172221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:29.372778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:29.535578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:29.696385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:29.892767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:30.058392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:30.400522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:30.577075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:30.742856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:30.916743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:31.091642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:31.305399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:31.538291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:31.715471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:31.887782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:32.057453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:32.219021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:32.387532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:32.562720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:32.719465image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:32.902597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:33.083742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:33.245119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:33.429349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:33.574719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:33.726233image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:33.893247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:34.052487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:34.213445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:34.381625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:34.535420image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:34.714629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-14T16:20:34.887037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-03-14T16:20:55.580963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-14T16:20:55.949861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-14T16:20:56.261871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-14T16:20:56.611845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-14T16:20:56.888010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-14T16:20:35.421628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-14T16:20:37.127244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-14T16:20:37.670944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-14T16:20:37.935648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

endTimeartistNametrackNamemsPlayedartist+trackid_album_typealbum_idalbum_namealbum_release_dateartist1_idartist1_nameartist1_typeartist2_idartist2_nameartist2_typeduration_msis_localnamepopularitytypedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
02020-01-22 11:54:00Ludwig van BeethovenString Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro244042Ludwig van Beethoven String Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro7vR6QjmkczDUUTQwhpeUdMalbum6ClMVcmFacF795m4jJZRpxBeethoven for Brainpower2021-01-192wOqMjp9TyABvtHdOSOTUSLudwig van Beethovenartist1mIuhlEoWepcSBZp60nd57LaSalle Quartetartist399160FalseString Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro1track0.4180.15706-16.15800.04720.9600.58500.08690.2930129.6444
12020-01-22 12:00:00Ludwig van BeethovenString Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro155099Ludwig van Beethoven String Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro7vR6QjmkczDUUTQwhpeUdMalbum6ClMVcmFacF795m4jJZRpxBeethoven for Brainpower2021-01-192wOqMjp9TyABvtHdOSOTUSLudwig van Beethovenartist1mIuhlEoWepcSBZp60nd57LaSalle Quartetartist399160FalseString Quartet No. 14 in C-Sharp Minor, Op. 131: 7. Allegro1track0.4180.15706-16.15800.04720.9600.58500.08690.2930129.6444
22020-01-22 12:03:00François CouperinSœur Monique: 18ème ordre, 3ème livre188100François Couperin Sœur Monique: 18ème ordre, 3ème livre5CBXjtjqToJ3sw0nCPdVDUalbum3qsB60Pmng0d7kg1hZHIzBCouperin: Les Ombres Errantes2013-02-254kVLtXuKJTE7SgYd2sQtaLFrançois Couperinartist6ACU5c6yaqQdW5u8bRNCvWIddo Bar-Shaïartist188229FalseSœur Monique: 18ème ordre, 3ème livre44track0.3540.06695-26.15410.03950.9890.93600.08270.316070.6034
32020-01-22 12:06:00Ludwig van BeethovenSeptet in E-Flat Major, Op. 20: III. Tempo di menuetto189863Ludwig van Beethoven Septet in E-Flat Major, Op. 20: III. Tempo di menuetto2GitKhuY13f5ZLfIXVJcNUalbum6ClMVcmFacF795m4jJZRpxBeethoven for Brainpower2021-01-192wOqMjp9TyABvtHdOSOTUSLudwig van Beethovenartist3ldMTcot7OqW9irqQMP0wuWiener Philharmonisches Kammerensembleartist212000FalseSeptet in E-Flat Major, Op. 20: III. Tempo di Menuetto1track0.4090.037110-22.05910.03510.9810.04190.13100.2990110.2734
42020-01-22 12:09:00Ludwig van BeethovenPiano Sonata No.20 in G Major, Op. 49 No.2: II. Tempo di Menuetto180111Ludwig van Beethoven Piano Sonata No.20 in G Major, Op. 49 No.2: II. Tempo di Menuetto3nnf7oDfeQ2zsnGFA3AhOPalbum16QCBn9N48I2Dv8K31K5RLA Day with Beethoven2021-01-152wOqMjp9TyABvtHdOSOTUSLudwig van Beethovenartist5w8T4OOA2gPqsga2b69563Yves Natartist160360FalsePiano Sonata No. 20 in G Major, Op. 49 No. 2: II. Tempo di Menuetto2track0.3730.11507-18.24210.03860.9910.85600.15200.4370143.5043
52020-01-22 12:14:00Johann Sebastian BachBach, JS / Orch. Marriner: Was mir behagt, ist nur die muntre Jagd, BWV 208: IX. Schafe können sicher weiden279353Johann Sebastian Bach Bach, JS / Orch. Marriner: Was mir behagt, ist nur die muntre Jagd, BWV 208: IX. Schafe können sicher weiden2JOXTg7PdMetphuHjckQHIalbum36WjHyPNQ0Itp7qlZ9KWkRThe Best of the Academy1995-01-015aIqB5nVVvmFsvSdExz408Johann Sebastian Bachartist6NUhQz7eAEsZvjEHTKHux9Sir Neville Marrinerartist277440FalseBach, JS / Orch. Marriner: Was mir behagt, ist nur die muntre Jagd, BWV 208: IX. Schafe können sicher weiden50track0.2260.065210-24.43010.04460.9560.92600.11100.184076.3544
62020-01-22 12:17:00Francesco GeminianiConcerto grosso, Op. 2, No. 2 in C Minor: IV. Allegro204113Francesco Geminiani Concerto grosso, Op. 2, No. 2 in C Minor: IV. Allegro2MqjdlvIAV35A8yx8frEdValbum6upCeDdeXiRHyXfWXLbNrUGeminiani: Quinta Essentia2019-09-204Vn6axeFKEaPRH1sI7JVKjFrancesco Geminianiartist6dxdut9Mhu7r7SM9HZB5ObConcerto Kölnartist204187FalseConcerto grosso, Op. 2, No. 2 in C Minor: IV. Allegro38track0.5190.115011-21.16400.03840.8840.04200.11000.6410124.7374
72020-01-22 12:22:00Victor MasséLes noces de Jeanette - opéra-comique : Air du rossignol267599Victor Massé Les noces de Jeanette - opéra-comique : Air du rossignol2mrejQjtvJVGzAQiS1PacUalbum1J4hDdBCCKW7vmB0M22JO3The Flight of the Bumblebee - Spring Music2000-01-011uJnvXZTIXWPODbfCuZNzAVictor Masséartist13nrWU0iAugxOj586gGfawPatrick Galloisartist268173FalseLes noces de Jeanette - opéra-comique: Air du rossignol19track0.3410.05858-25.80310.05170.9720.90700.10400.0759124.4594
82020-01-22 12:25:00Carl Philipp Emanuel BachKeyboard Concerto in G Major, Wq. 44: II. Andantino198357Carl Philipp Emanuel Bach Keyboard Concerto in G Major, Wq. 44: II. Andantino7xI0wxBQhYMSWDhkuvbRgMalbum4PWG4HAlwOceVdbjiX3r08C.P.E. Bach: Keyboard Concertos, Wq. 26, 44 & 202016-01-013meioy7GWDwpwmjv2LPyAbCarl Philipp Emanuel Bachartist2m1oXzullfjrEtsT2z8WiaMichael Rischeartist198333FalseKeyboard Concerto in G Major, Wq. 44: II. Andantino40track0.3840.02744-26.64000.03390.9750.62300.23000.196086.9484
92020-01-22 12:27:00Emmanuel PahudMolino: Duo No. 3, Op. 16: I. Andantino97837Emmanuel Pahud Molino: Duo No. 3, Op. 16: I. Andantino15ZcZqeTTpIy7RDs8P8DuHalbum6a5OK0q8lpZW1hQgJn9fCmAround The World2013-10-076duaKoFWgzQNgHlfmn3dHqEmmanuel Pahudartist4Uz03jdkyGrQHyHepr1KqkChristian Rivetartist98320FalseMolino: Duo No. 3, Op. 16: I. Andantino42track0.2380.04147-25.18010.04520.9830.74200.09890.3250167.1864

Last rows

endTimeartistNametrackNamemsPlayedartist+trackid_album_typealbum_idalbum_namealbum_release_dateartist1_idartist1_nameartist1_typeartist2_idartist2_nameartist2_typeduration_msis_localnamepopularitytypedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
94212021-01-19 14:52:00Sigur RósBrennisteinn3091Sigur Rós Brennisteinn6xuaTUusUQ3q35YKhmpw7Xalbum1rRNqn4MTZfh393GzGvBtYKveikur2013-06-076UUrUCIZtQeOf8tC0WuzRySigur RósartistNaNNaNNaN464666FalseBrennisteinn40track0.3610.8240-6.03700.06310.0005910.0486000.10600.0985120.6664
94222021-01-20 10:52:00Sigur RósBrennisteinn464494Sigur Rós Brennisteinn6xuaTUusUQ3q35YKhmpw7Xalbum1rRNqn4MTZfh393GzGvBtYKveikur2013-06-076UUrUCIZtQeOf8tC0WuzRySigur RósartistNaNNaNNaN464666FalseBrennisteinn40track0.3610.8240-6.03700.06310.0005910.0486000.10600.0985120.6664
94232021-01-19 14:29:00Sigur RósHoppípolla269889Sigur Rós Hoppípolla6eTGxxQxiTFE6LfZHC33Wmalbum3sE83l3A58DipFp3EzNLiETakk...2005-08-226UUrUCIZtQeOf8tC0WuzRySigur RósartistNaNNaNNaN270013FalseHoppípolla57track0.3120.54311-9.87510.03080.0356000.2250000.12800.1780140.1094
94242021-01-19 15:16:00ППКВоскрешение (Robot's Outro)354ППК Воскрешение (Robot's Outro)6KewhLhocky21TwVIb8oxLalbum1nmhnokgCVl70c7e2KX8TEРусский транс: Формирование2018-06-116kGGvTWOWaF26tZF5hy19zPPKartistNaNNaNNaN101586FalseВоскрешение (Robot's Outro)24track0.5460.4382-11.73300.05140.9930000.9620000.11500.8000138.5584
94252021-01-19 15:22:00GarbageStupid Girl - 2015 - Remaster181Garbage Stupid Girl - 2015 - Remaster6ErqY5wBBbZKjY9FheYwcIalbum6J6VzS5nLEzIdAynrYzqfkGarbage (20th Anniversary Edition)1995-08-156S0GHTqz5sxK5f9HtLXn9qGarbageartistNaNNaNNaN257756FalseStupid Girl - 2015 - Remaster47track0.6320.7601-4.79800.03420.0024500.0004660.04940.6710119.9504
94262021-01-19 15:23:00Franz FerdinandStand On the Horizon1609Franz Ferdinand Stand On the Horizon7mexukWpLrCVwAztkM8kQlalbum5W7631WJASasWPAFFzDpziRight Thoughts, Right Words, Right Action (Deluxe Edition)2013-08-260XNa1vTidXlvJ2gHSsRi4AFranz FerdinandartistNaNNaNNaN263120FalseStand On the Horizon43track0.6600.8274-4.01500.04410.0996000.0000000.11500.5760123.3644
94272021-01-19 15:36:00GorillazOrchestral Intro (feat. Sinfonia ViVA)69338Gorillaz Orchestral Intro (feat. Sinfonia ViVA)3s2f6XU5sJ6aqEe7GgDBtaalbum2dIGnmEIy1WZIcZCFSj6i8Plastic Beach2010-03-033AA28KZvwAUcZuOKwyblJQGorillazartist4Vt6rW97rpUAJmi3MZT5laSinfonia ViVaartist69386FalseOrchestral Intro (feat. Sinfonia ViVA)50track0.1070.3764-12.34210.03970.9820000.9050000.34700.0334161.4203
94282021-01-19 15:39:00GorillazWelcome to the World of the Plastic Beach (feat. Snoop Dogg and Hypnotic Brass Ensemble)215333Gorillaz Welcome to the World of the Plastic Beach (feat. Snoop Dogg and Hypnotic Brass Ensemble)46WOptLnXUtH3LOyYvmMO1album2dIGnmEIy1WZIcZCFSj6i8Plastic Beach2010-03-033AA28KZvwAUcZuOKwyblJQGorillazartist4wvPcMJk7L9Rbw9Qh4kBeHHypnotic Brass Ensembleartist215506FalseWelcome to the World of the Plastic Beach (feat. Snoop Dogg and Hypnotic Brass Ensemble)53track0.6450.7772-7.23510.04180.0065300.0039000.11900.331088.4554
94292021-01-19 15:43:00GorillazWhite Flag (feat. Bashy, Kano and the National Orchestra for Arabic Music)223349Gorillaz White Flag (feat. Bashy, Kano and the National Orchestra for Arabic Music)3famfyGuWw5QFcVO5Xk1uWalbum2dIGnmEIy1WZIcZCFSj6i8Plastic Beach2010-03-033AA28KZvwAUcZuOKwyblJQGorillazartist2pkcEiIg9zyj8C7Zq5TumZBashyartist223466FalseWhite Flag (feat. Bashy, Kano and the National Orchestra for Arabic Music)52track0.7300.6390-6.98710.14200.0725000.0007950.17400.5630102.3334
94302021-01-20 00:08:00Sigur RósSvefn-g-englar29884Sigur Rós Svefn-g-englar07eGxuz8bL6QMsRqEe1Adualbum1DMMv1Kmoli3Y9fVEZDUVCÁgætis byrjun1999-06-126UUrUCIZtQeOf8tC0WuzRySigur RósartistNaNNaNNaN606850FalseSvefn-g-englar57track0.2000.4669-12.29810.04860.5570000.7020000.10800.0572136.9135